From d227de56c950f6997633a8bbbffb2209bf468052 Mon Sep 17 00:00:00 2001 From: I0308559 Date: Mon, 29 Jul 2019 09:51:32 +0200 Subject: [PATCH 1/9] Initial commit to migrate VRE branch from Sqooba BitBucket to Insel GitHub as master --- .gitignore | 299 ++++ .../inspectionProfiles/Project_Default.xml | 25 + spitalhygiene/.idea/misc.xml | 7 + spitalhygiene/.idea/modules.xml | 8 + spitalhygiene/.idea/spitalhygiene.iml | 15 + spitalhygiene/.idea/vcs.xml | 6 + spitalhygiene/Docs/Makefile | 19 + spitalhygiene/Docs/make.bat | 35 + .../Docs/source/Patient_Test_Data.rst | 258 ++++ spitalhygiene/Docs/source/VRE_Model.rst | 11 + spitalhygiene/Docs/source/conf.py | 205 +++ spitalhygiene/Docs/source/index.rst | 25 + spitalhygiene/Docs/source/resources.rst | 21 + spitalhygiene/Docs/source/sql.rst | 6 + spitalhygiene/Docs/source/vre.rst | 36 + spitalhygiene/README.md | 27 + spitalhygiene/pom.xml | 15 + .../AM aus PDMS_VRE_2018-08-18_fg.xlsx | Bin 0 -> 4827711 bytes spitalhygiene/resources/BWTYP-BWART.csv | 72 + spitalhygiene/resources/PDMS_ATC.csv | 1 + spitalhygiene/resources/Query_Atelier_Data.py | 100 ++ spitalhygiene/resources/Update_Model.sh | 102 ++ spitalhygiene/resources/query_device.sql | 20 + spitalhygiene/resources/tree_all.html | 169 +++ spitalhygiene/resources/tree_devices.html | 169 +++ spitalhygiene/resources/tree_employees.html | 169 +++ spitalhygiene/resources/tree_nomed.html | 169 +++ spitalhygiene/resources/tree_rooms.html | 169 +++ .../resources/umgebungsabklaerung.cypher | 10 + spitalhygiene/sql/quality/LA_ISH_NBEW_DQ.sql | 38 + .../sql/quality/LA_ISH_NDIA_NORM_DQ.sql | 13 + spitalhygiene/sql/quality/LA_ISH_NDRG_DQ.sql | 6 + spitalhygiene/sql/quality/LA_ISH_NFPZ_DQ.sql | 14 + spitalhygiene/sql/quality/LA_ISH_NGPA_DQ.sql | 22 + spitalhygiene/sql/quality/LA_ISH_NICP_DQ.sql | 20 + spitalhygiene/sql/quality/TACS_DATEN_DQ.sql | 26 + .../sql/quality/V_LA_IPD_DRUG_NORM_DQ.sql | 18 + .../sql/quality/V_LA_ISH_NFAL_NORM_DQ.sql | 20 + 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| 8 + spitalhygiene/sql/vre/LA_ISH_NBEW.sql | 20 + spitalhygiene/sql/vre/LA_ISH_NDIA_NORM.sql | 8 + spitalhygiene/sql/vre/LA_ISH_NDRG.sql | 4 + spitalhygiene/sql/vre/LA_ISH_NFPZ.sql | 8 + spitalhygiene/sql/vre/LA_ISH_NGPA.sql | 11 + spitalhygiene/sql/vre/LA_ISH_NICP.sql | 10 + spitalhygiene/sql/vre/TACS_DATEN.sql | 13 + spitalhygiene/sql/vre/V_DH_DIM_GERAET_CUR.sql | 3 + .../sql/vre/V_DH_DIM_PATIENT_CUR.sql | 14 + spitalhygiene/sql/vre/V_DH_DIM_RAUM_CUR.sql | 4 + spitalhygiene/sql/vre/V_DH_DIM_TERMIN_CUR.sql | 8 + .../sql/vre/V_DH_FACT_TERMINGERAET.sql | 6 + .../sql/vre/V_DH_FACT_TERMINMITARBEITER.sql | 6 + .../sql/vre/V_DH_FACT_TERMINPATIENT.sql | 4 + .../sql/vre/V_DH_FACT_TERMINRAUM.sql | 10 + spitalhygiene/sql/vre/V_DH_REF_CHOP.sql | 18 + spitalhygiene/sql/vre/V_LA_IPD_DRUG_NORM.sql | 10 + spitalhygiene/sql/vre/V_LA_ISH_NFAL_NORM.sql | 11 + spitalhygiene/sql/vre/V_LA_ISH_NRSF_NORM.sql | 8 + spitalhygiene/sql/vre/deleted_screenings.sql | 9 + spitalhygiene/vre/pom.xml | 32 + spitalhygiene/vre/src/main/python/README.md | 17 + spitalhygiene/vre/src/main/python/setup.py | 33 + .../vre/src/main/python/tests/conftest.py | 115 ++ .../vre/src/main/python/tests/pytest.ini | 7 + .../vre/src/main/python/tests/test_age.py | 3 + .../python/tests/test_antibiotic_exposure.py | 36 + .../main/python/tests/test_appointments.py | 5 + .../vre/src/main/python/tests/test_care.py | 10 + .../src/main/python/tests/test_case_timing.py | 117 ++ .../src/main/python/tests/test_chop_codes.py | 28 + .../vre/src/main/python/tests/test_devices.py | 7 + .../src/main/python/tests/test_employees.py | 7 + .../src/main/python/tests/test_features.py | 64 + .../src/main/python/tests/test_icu_stay.py | 5 + .../src/main/python/tests/test_idc_codes.py | 17 + .../vre/src/main/python/tests/test_label.py | 7 + .../src/main/python/tests/test_load_data.py | 56 + .../main/python/tests/test_model_creator.py | 53 + .../src/main/python/tests/test_nr_cases.py | 2 + .../src/main/python/tests/test_partners.py | 7 + .../main/python/tests/test_relevant_rooms.py | 21 + .../src/main/python/tests/test_risk_data.py | 33 + .../vre/src/main/python/vre/BasicConfig.ini | 40 + .../src/main/python/vre/HDFS_data_loader.py | 283 ++++ .../vre/src/main/python/vre/Neo4JExporter.py | 463 ++++++ .../vre/src/main/python/vre/README.md | 91 ++ .../vre/src/main/python/vre/data_compiler.py | 268 ++++ .../src/main/python/vre/feature_extractor.py | 287 ++++ .../src/main/python/vre/model/Appointment.py | 100 ++ .../vre/src/main/python/vre/model/Bed.py | 8 + .../vre/src/main/python/vre/model/Care.py | 83 ++ .../vre/src/main/python/vre/model/Case.py | 208 +++ .../vre/src/main/python/vre/model/Chop.py | 112 ++ .../vre/src/main/python/vre/model/Device.py | 60 + .../vre/src/main/python/vre/model/Employee.py | 57 + .../vre/src/main/python/vre/model/ICD.py | 71 + .../src/main/python/vre/model/Medication.py | 76 + .../vre/src/main/python/vre/model/Move.py | 154 ++ .../vre/src/main/python/vre/model/Partner.py | 97 ++ .../vre/src/main/python/vre/model/Patient.py | 464 ++++++ .../vre/src/main/python/vre/model/Risk.py | 184 +++ .../vre/src/main/python/vre/model/Room.py | 153 ++ .../vre/src/main/python/vre/model/Surgery.py | 62 + .../vre/src/main/python/vre/model/Ward.py | 19 + .../vre/src/main/python/vre/model/__init__.py | 0 .../vre/src/main/python/vre/networkx_graph.py | 1257 +++++++++++++++++ .../vre/src/main/python/vre/preprocessor.py | 200 +++ .../python/vre/quality_control/__init__.py | 0 .../vre/quality_control/data_purification.py | 64 + .../quality_control/distribution_export.py | 133 ++ spitalhygiene/vre/src/main/python/vre/test.py | 18 + .../vre/src/main/python/vre/test2.py | 160 +++ 132 files changed, 9281 insertions(+) create mode 100644 .gitignore create mode 100644 spitalhygiene/.idea/inspectionProfiles/Project_Default.xml create mode 100644 spitalhygiene/.idea/misc.xml create mode 100644 spitalhygiene/.idea/modules.xml create mode 100644 spitalhygiene/.idea/spitalhygiene.iml create mode 100644 spitalhygiene/.idea/vcs.xml create mode 100644 spitalhygiene/Docs/Makefile create mode 100644 spitalhygiene/Docs/make.bat create mode 100644 spitalhygiene/Docs/source/Patient_Test_Data.rst create mode 100644 spitalhygiene/Docs/source/VRE_Model.rst create mode 100644 spitalhygiene/Docs/source/conf.py create mode 100644 spitalhygiene/Docs/source/index.rst create mode 100644 spitalhygiene/Docs/source/resources.rst create mode 100644 spitalhygiene/Docs/source/sql.rst create mode 100644 spitalhygiene/Docs/source/vre.rst create mode 100644 spitalhygiene/README.md create mode 100644 spitalhygiene/pom.xml create mode 100644 spitalhygiene/resources/AM aus PDMS_VRE_2018-08-18_fg.xlsx create mode 100644 spitalhygiene/resources/BWTYP-BWART.csv create mode 100644 spitalhygiene/resources/PDMS_ATC.csv create mode 100644 spitalhygiene/resources/Query_Atelier_Data.py create mode 100644 spitalhygiene/resources/Update_Model.sh create mode 100644 spitalhygiene/resources/query_device.sql create mode 100644 spitalhygiene/resources/tree_all.html create mode 100644 spitalhygiene/resources/tree_devices.html create mode 100644 spitalhygiene/resources/tree_employees.html create mode 100644 spitalhygiene/resources/tree_nomed.html create mode 100644 spitalhygiene/resources/tree_rooms.html create mode 100644 spitalhygiene/resources/umgebungsabklaerung.cypher create mode 100644 spitalhygiene/sql/quality/LA_ISH_NBEW_DQ.sql create mode 100644 spitalhygiene/sql/quality/LA_ISH_NDIA_NORM_DQ.sql create mode 100644 spitalhygiene/sql/quality/LA_ISH_NDRG_DQ.sql create mode 100644 spitalhygiene/sql/quality/LA_ISH_NFPZ_DQ.sql create mode 100644 spitalhygiene/sql/quality/LA_ISH_NGPA_DQ.sql create mode 100644 spitalhygiene/sql/quality/LA_ISH_NICP_DQ.sql create mode 100644 spitalhygiene/sql/quality/TACS_DATEN_DQ.sql create mode 100644 spitalhygiene/sql/quality/V_LA_IPD_DRUG_NORM_DQ.sql create mode 100644 spitalhygiene/sql/quality/V_LA_ISH_NFAL_NORM_DQ.sql create mode 100644 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create mode 100644 spitalhygiene/vre/src/main/python/vre/model/__init__.py create mode 100644 spitalhygiene/vre/src/main/python/vre/networkx_graph.py create mode 100644 spitalhygiene/vre/src/main/python/vre/preprocessor.py create mode 100644 spitalhygiene/vre/src/main/python/vre/quality_control/__init__.py create mode 100644 spitalhygiene/vre/src/main/python/vre/quality_control/data_purification.py create mode 100644 spitalhygiene/vre/src/main/python/vre/quality_control/distribution_export.py create mode 100644 spitalhygiene/vre/src/main/python/vre/test.py create mode 100644 spitalhygiene/vre/src/main/python/vre/test2.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..128abd9 --- /dev/null +++ b/.gitignore @@ -0,0 +1,299 @@ + +# Created by https://www.gitignore.io/api/macos,python,pycharm,intellij +# Edit at https://www.gitignore.io/?templates=macos,python,pycharm,intellij + +### Intellij ### +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# Ignore .idea directory +/spitalhygiene/.idea/* + + +# User-specific stuff +# .idea/**/workspace.xml +# .idea/**/tasks.xml +# .idea/**/usage.statistics.xml +# .idea/**/dictionaries +# .idea/**/shelf + +# Generated files +# .idea/**/contentModel.xml + +# Sensitive or high-churn files +# .idea/**/dataSources/ +# .idea/**/dataSources.ids +# .idea/**/dataSources.local.xml +# .idea/**/sqlDataSources.xml +# .idea/**/dynamic.xml +# .idea/**/uiDesigner.xml +# .idea/**/dbnavigator.xml + +# Gradle +# .idea/**/gradle.xml +# .idea/**/libraries + +# Gradle and Maven with auto-import +# When using Gradle or Maven with auto-import, you should exclude module files, +# since they will be recreated, and may cause churn. Uncomment if using +# auto-import. +# .idea/modules.xml +# .idea/*.iml +# .idea/modules + +# CMake +cmake-build-*/ + +# Mongo Explorer plugin +# .idea/**/mongoSettings.xml + +# File-based project format +*.iws + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +# .idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties + +# Editor-based Rest Client +# .idea/httpRequests + +# Android studio 3.1+ serialized cache file +# .idea/caches/build_file_checksums.ser + +### Intellij Patch ### +# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721 + +# *.iml +# modules.xml +# .idea/misc.xml +# *.ipr + +# Sonarlint plugin +.idea/sonarlint + +### macOS ### +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + +### PyCharm ### +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff + +# Generated files + +# Sensitive or high-churn files + +# Gradle + +# Gradle and Maven with auto-import +# When using Gradle or Maven with auto-import, you should exclude module files, +# since they will be recreated, and may cause churn. Uncomment if using +# auto-import. +# .idea/modules.xml +# .idea/*.iml +# .idea/modules + +# CMake + +# Mongo Explorer plugin + +# File-based project format + +# IntelliJ + +# mpeltonen/sbt-idea plugin + +# JIRA plugin + +# Cursive Clojure plugin + +# Crashlytics plugin (for Android Studio and IntelliJ) + +# Editor-based Rest Client + +# Android studio 3.1+ serialized cache file + +### PyCharm Patch ### +# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721 + +# *.iml +# modules.xml +# .idea/misc.xml +# *.ipr + +# Sonarlint plugin + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +### Python Patch ### +.venv/ + +### Python.VirtualEnv Stack ### +# Virtualenv +# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/ +[Bb]in +[Ii]nclude +[Ll]ib +[Ll]ib64 +[Ll]ocal +[Ss]cripts +pyvenv.cfg +pip-selfcheck.json + +# End of https://www.gitignore.io/api/macos,python,pycharm,intellij + +*testfiles* diff --git a/spitalhygiene/.idea/inspectionProfiles/Project_Default.xml b/spitalhygiene/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..3cf9310 --- /dev/null +++ b/spitalhygiene/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,25 @@ + + + + \ No newline at end of file diff --git a/spitalhygiene/.idea/misc.xml b/spitalhygiene/.idea/misc.xml new file mode 100644 index 0000000..b158bc4 --- /dev/null +++ b/spitalhygiene/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/spitalhygiene/.idea/modules.xml b/spitalhygiene/.idea/modules.xml new file mode 100644 index 0000000..a8bfa45 --- /dev/null +++ b/spitalhygiene/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/spitalhygiene/.idea/spitalhygiene.iml b/spitalhygiene/.idea/spitalhygiene.iml new file mode 100644 index 0000000..50fbfa5 --- /dev/null +++ b/spitalhygiene/.idea/spitalhygiene.iml @@ -0,0 +1,15 @@ + + + + + + + + + + + + \ No newline at end of file diff --git a/spitalhygiene/.idea/vcs.xml b/spitalhygiene/.idea/vcs.xml new file mode 100644 index 0000000..6c0b863 --- /dev/null +++ b/spitalhygiene/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/spitalhygiene/Docs/Makefile b/spitalhygiene/Docs/Makefile new file mode 100644 index 0000000..69fe55e --- /dev/null +++ b/spitalhygiene/Docs/Makefile @@ -0,0 +1,19 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/spitalhygiene/Docs/make.bat b/spitalhygiene/Docs/make.bat new file mode 100644 index 0000000..4d9eb83 --- /dev/null +++ b/spitalhygiene/Docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd diff --git a/spitalhygiene/Docs/source/Patient_Test_Data.rst b/spitalhygiene/Docs/source/Patient_Test_Data.rst new file mode 100644 index 0000000..1bb2794 --- /dev/null +++ b/spitalhygiene/Docs/source/Patient_Test_Data.rst @@ -0,0 +1,258 @@ +******************** +Patient Test Data +******************** + +The test patient dataset for the VRE model consists of a small subset of the complete dataset. The SQL queries used to +extract the test patient data are `identical` to those used for the true dataset, with the exception that data are +restricted to a small subset in the form of a simple ``WHERE`` statement. This filter includes the following 3 patient +IDs: + +- 00003067149 +- 00008301433 +- 00004348346 + +Associated with these patients are the following 40 case IDs: + +=========== =========== +Patient ID Case ID +=========== =========== +00003067149 0006280483 +00003067149 0006314210 +00003067149 0006336438 +00003067149 0005889802 +00003067149 0005873082 +00003067149 0006065973 +00003067149 0006091736 +00003067149 0006148746 +00003067149 0006334066 +00003067149 0006059391 +00003067149 0005976205 +00003067149 0006057834 +00003067149 0005983693 +00003067149 0006520444 +00003067149 0006931777 +00003067149 0006812114 +00003067149 0005965462 +00003067149 0006452545 +00003067149 0006433446 +00003067149 0006466165 +00004348346 0001927876 +00004348346 0004555507 +00004348346 0004728517 +00004348346 0001928507 +00004348346 0002802610 +00004348346 0004204668 +00004348346 0004181978 +00003067149 0006951942 +00003067149 0005880782 +00008301433 0002289902 +00008301433 0004411153 +00008301433 0004411005 +00008301433 0006565152 +00008301433 0003962974 +00008301433 0006594482 +00008301433 0006596375 +00008301433 0006551728 +00003067149 0005864325 +00003067149 0005877026 +00003067149 0006069476 +=========== =========== + +For partners, only a single entry associated to one of the three test patients is used: + +- 0010000990 + +For CHOP codes, the following sub-selection of codes will be imported: + +- Z99.B8.11 +- Z50.27.32 +- Z00.99.60 +- Z88.38.60 +- Z99.85 +- Z89.07.24 +- Z50.27.32 +- Z00.99.60 +- Z88.38.60 +- Z99.85 +- Z50.23.13 +- Z50.12.09 +- Z88.79.50 +- Z00.9A.13 +- Z39.32.41 +- Z50.93 +- Z34.84 +- Z34.89.99 +- Z39.29.89 +- Z50.52 +- Z00.93.99 +- Z00.90.99 +- Z00.99.10 +- Z50.27.32 +- Z00.99.60 +- Z88.38.60 +- Z99.85 +- Z99.04.10 +- Z94.8X.40 +- Z99.B7.12 +- Z99.07.3C +- Z99.04.15 +- Z99.05.47 +- Z99.B7.13 +- Z99.0A +- Z99.28.11 +- Z50.52 +- Z54.52 +- Z00.93.99 +- Z00.90.99 +- Z51.22.11 +- Z39.29.89 +- Z99.00 +- Z54.12.11 +- Z50.12.12 +- Z88.79.50 +- Z54.25 +- Z36.11.22 +- Z36.11.26 +- Z36.1C.12 +- Z39.61.10 +- Z39.63 +- Z39.64 +- Z88.79.50 +- Z01.16.12 +- Z99.00 + +For appointments (german: "Termine"), only the following 98 TerminIDs are used: + +- 38515699 +- 38321122 +- 35416924 +- 1164130 +- 38470639 +- 41827160 +- 39893063 +- 38411180 +- 35571391 +- 35130813 +- 36160483 +- 40766840 +- 42155710 +- 39491988 +- 36067632 +- 37374631 +- 36129549 +- 39001478 +- 39425469 +- 34338471 +- 35630084 +- 35139096 +- 38431954 +- 38452040 +- 40344805 +- 13831398 +- 38063644 +- 38539785 +- 34220024 +- 39819467 +- 39423020 +- 38386995 +- 42394432 +- 38446243 +- 42213628 +- 38565198 +- 39893320 +- 37244357 +- 37554138 +- 41124954 +- 39051017 +- 36129560 +- 35621237 +- 38772701 +- 21130116 +- 38063650 +- 39608858 +- 39427731 +- 21131159 +- 38331618 +- 38062724 +- 24171386 +- 14908956 +- 41909560 +- 39114133 +- 14091256 +- 38939623 +- 35626775 +- 35139491 +- 36006751 +- 38329080 +- 41909690 +- 35130747 +- 36129541 +- 1278803 +- 38507433 +- 1192059 +- 39456191 +- 14091249 +- 39933520 +- 24291359 +- 36071093 +- 36160474 +- 19096210 +- 40218521 +- 1162144 +- 38660148 +- 42211133 +- 39613790 +- 24230235 +- 38262758 +- 35417252 +- 19252406 +- 39215737 +- 38446041 +- 36830543 +- 35200182 +- 40766156 +- 36070942 +- 34310589 +- 37232112 +- 34337667 +- 38446523 +- 34482529 +- 17297480 +- 39298995 +- 36830574 +- 1405150 + +And finally for devices, the subset is restricted to the following GeraetIDs: + +- 134074 +- 125922 +- 137160 +- 125916 +- 125981 +- 125981 +- 64174 +- 125921 +- 125981 +- 125981 +- 125981 +- 125981 +- 125981 +- 28609 +- 86293 +- 125981 +- 125981 +- 125981 +- 86293 +- 125981 +- 125981 +- 64174 +- 125981 +- 125981 +- 125981 +- 125981 +- 125981 +- 125974 +- 28609 +- 125981 + +------ diff --git a/spitalhygiene/Docs/source/VRE_Model.rst b/spitalhygiene/Docs/source/VRE_Model.rst new file mode 100644 index 0000000..669f4e5 --- /dev/null +++ b/spitalhygiene/Docs/source/VRE_Model.rst @@ -0,0 +1,11 @@ +******************** +VRE Model Overview +******************** + +Welcome to the VRE Model Documentation! + +The VRE model is documented with `Sphinx` on a `per-folder` basis. The root folder for this documentation is the +``spitalhygiene`` folder of the ``insel`` mono-repo on BitBucket. + + + diff --git a/spitalhygiene/Docs/source/conf.py b/spitalhygiene/Docs/source/conf.py new file mode 100644 index 0000000..a9bf449 --- /dev/null +++ b/spitalhygiene/Docs/source/conf.py @@ -0,0 +1,205 @@ +# -*- coding: utf-8 -*- +# +# Configuration file for the Sphinx documentation builder. +# +# This file does only contain a selection of the most common options. For a +# full list see the documentation: +# http://www.sphinx-doc.org/en/master/config + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys +sys.path.insert(0, os.path.abspath('../../resources')) +sys.path.insert(0, os.path.abspath('../../vre')) +sys.path.insert(0, os.path.abspath('../../vre/src/main/python/vre')) + + +# -- Project information ----------------------------------------------------- + +project = 'VRE Model' +copyright = '2019, SZ' +author = 'SZ' + +# The short X.Y version +version = '' +# The full version, including alpha/beta/rc tags +release = '0.0.1' + + +# -- General configuration --------------------------------------------------- + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.doctest', + 'sphinx.ext.todo', + 'sphinx.ext.coverage', + 'sphinx.ext.mathjax', + 'sphinx.ext.githubpages', + 'sphinxcontrib.napoleon', +] + +# Napoleon settings +napoleon_google_docstring = True +napoleon_numpy_docstring = True +napoleon_include_init_with_doc = False +napoleon_include_private_with_doc = False +napoleon_include_special_with_doc = False +napoleon_use_admonition_for_examples = False +napoleon_use_admonition_for_notes = False +napoleon_use_admonition_for_references = False +napoleon_use_ivar = False +napoleon_use_param = True +napoleon_use_rtype = True +napoleon_use_keyword = True +napoleon_custom_sections = None + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The master toctree document. +master_doc = 'index' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = None + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +# html_sidebars = {} + + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = 'VREModeldoc' + + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'VREModel.tex', 'VRE Model Documentation', + 'SZ', 'manual'), +] + + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'vremodel', 'VRE Model Documentation', + [author], 1) +] + + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'VREModel', 'VRE Model Documentation', + author, 'VREModel', 'One line description of project.', + 'Miscellaneous'), +] + + +# -- Options for Epub output ------------------------------------------------- + +# Bibliographic Dublin Core info. +epub_title = project + +# The unique identifier of the text. This can be a ISBN number +# or the project homepage. +# +# epub_identifier = '' + +# A unique identification for the text. +# +# epub_uid = '' + +# A list of files that should not be packed into the epub file. +epub_exclude_files = ['search.html'] + + +# -- Extension configuration ------------------------------------------------- + +# -- Options for todo extension ---------------------------------------------- + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = True diff --git a/spitalhygiene/Docs/source/index.rst b/spitalhygiene/Docs/source/index.rst new file mode 100644 index 0000000..f06dcca --- /dev/null +++ b/spitalhygiene/Docs/source/index.rst @@ -0,0 +1,25 @@ +.. VRE Model documentation master file, created by + sphinx-quickstart on Tue Apr 23 15:53:29 2019. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to the VRE Model documentation! +======================================= + +.. toctree:: + :caption: Contents: + + VRE_Model.rst + resources.rst + sql.rst + vre.rst + Patient_Test_Data.rst + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/spitalhygiene/Docs/source/resources.rst b/spitalhygiene/Docs/source/resources.rst new file mode 100644 index 0000000..86816d6 --- /dev/null +++ b/spitalhygiene/Docs/source/resources.rst @@ -0,0 +1,21 @@ +************************ +``resources`` folder +************************ + +This folder contains important functions for loading data from SQL into CSV, thereby preparing the "raw" data used for +building the actual network models. + +Most importantly, this folder also contains the file ``Update_Model.sh``, which is a bash script controlling `all steps` +in the VRE calculation. Since all VRE data are recalculated once per day, this includes (in order): + +1) Backing up data from the previous calculation cycle in the HDFS file system (from step 4 in the previous run) +2) Reloading data from SQL into CSV +3) Running the VRE analysis (all steps of the analysis are controlled with ``feature_extractor.py``) +4) Adding new data (SQL files, CSV files, other data) to the HDFS file system + + +File: Query_Atelier_Data.py +=============================== + +.. automodule:: Query_Atelier_Data + :members: \ No newline at end of file diff --git a/spitalhygiene/Docs/source/sql.rst b/spitalhygiene/Docs/source/sql.rst new file mode 100644 index 0000000..4ccc191 --- /dev/null +++ b/spitalhygiene/Docs/source/sql.rst @@ -0,0 +1,6 @@ +************************ +``sql`` folder +************************ + +This folder contains important functions for extracting SQL data. + diff --git a/spitalhygiene/Docs/source/vre.rst b/spitalhygiene/Docs/source/vre.rst new file mode 100644 index 0000000..15213bf --- /dev/null +++ b/spitalhygiene/Docs/source/vre.rst @@ -0,0 +1,36 @@ +***************************************** +``vre/src/main/python/vre`` folder +***************************************** + +This folder contains all files relevant for building the actual VRE model. + + +----- + +File: data_compiler.py +=============================== + +.. automodule:: data_compiler + :members: + + +File: feature_extractor.py +=============================== + +.. automodule:: feature_extractor + :members: + + +File: HDFS_data_loader.py +=============================== + +.. automodule:: HDFS_data_loader + :members: + + +File: networkx_graph.py +=============================== + +.. automodule:: networkx_graph + :members: + diff --git a/spitalhygiene/README.md b/spitalhygiene/README.md new file mode 100644 index 0000000..0fe7988 --- /dev/null +++ b/spitalhygiene/README.md @@ -0,0 +1,27 @@ +# Directory Overview + +Folder `vre` + +Contains all functions relevant for data processing, model creation, feature vector export, and testing. + +----- + +Folder `sql`: + +Contains 3 folders: + - `tests` → Contains SQL queries yielding the test dataset + - `vre` → Contains SQL queries yielding the complete model dataset + - `quality` → Contains SQL queries yielding data used for quality assessment + +----- + +Folder `resources`: + +Contains various output files from the model and the `update_db.*` scripts, which control the querying and export of data from Atelier_DataScience to CSV. + +----- + +Folder `Docs`: + +Contains the Sphinx documentation for the entire VRE model code. + diff --git a/spitalhygiene/pom.xml b/spitalhygiene/pom.xml new file mode 100644 index 0000000..550d910 --- /dev/null +++ b/spitalhygiene/pom.xml @@ -0,0 +1,15 @@ + + + 4.0.0 + io.sqooba.insel + spitalhygiene + 0.1.0-SNAPSHOT + pom + + + vre + + + + diff --git a/spitalhygiene/resources/AM aus PDMS_VRE_2018-08-18_fg.xlsx b/spitalhygiene/resources/AM aus PDMS_VRE_2018-08-18_fg.xlsx new file mode 100644 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     + + +
    +

    File: Bed.py

    +

    This script contains the Bed class used in the VRE model.

    +
    +
    +
    +class Bed.Bed(name)
    +

    Models a Bed.

    +
    +
    +add_move(move)
    +

    Adds a move to the self.moves() list of this bed.

    +
    +
    Parameters
    +

    move (Move() Object) – Move() object to append.

    +
    +
    +
    + +
    + +
    +
    +

    File: Care.py

    +

    This script contains the Care class used in the VRE model.

    +
    +
    +
    +class Care.Care(patient_patientid, patient_typ, patient_status, fall_nummer, fall_typ, fall_status, datum_betreuung, dauer_betreuung_in_min, mitarbeiter_personalnummer, mitarbeiter_anstellungsnummer, mitarbeiter_login, batch_run_id)
    +

    Models an entry in TACS.

    +
    +
    +static add_care_to_case(lines, cases, employees)
    +

    Adds the entries from TACS as instances of Care() objects to the respective Case().

    +

    This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). +The underlying table in the Atelier_DataScience is called TACS_DATEN and structured as follows:

    + ++++++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    patient_patientid

    patient_typ

    patient_status

    fall_nummer

    fall_typ

    fall_status

    datum_betreuung

    dauer_betreuung_in_min

    mitarbeiter_personalnummer

    mitarbeiter_anstellungsnummer

    mitarbeiter_login

    BATCH_RUN_ID

    00013768220

    Standard Patient

    aktiv

    0006422111

    Standard Fall

    aktiv

    2018-03-11 00:00:00.0

    3

    0301119

    00026556

    I0301119

    870

    00000552828

    Standard Patient

    aktiv

    0006454306

    Standard Fall

    aktiv

    2018-04-10 00:00:00.0

    20

    0025908

    00014648

    I0025908

    870

    +
    +
    Parameters
    +
      +

    • lines (iterator() object) – csv iterator from which data will be read

      • +

    • cases (dict) –

      Dictionary mapping case ids to Case() objects

      +

      –> {"0003536421" : Case(), "0003473241" : Case(), ...}

      +

      • +

    • employees (dict) –

      Dictionary mapping employee_ids to Employee() objects

      +

      –> {'0032719' : Employee(), ... }

      +

      • +
    +
    +
    +
    + +
    +
    +add_employee(employee)
    +

    Assigns an employee to the self.employee attribute.

    +
    +

    Note

    +

    Only one employee can be assigned to Care() objects!

    +
    +
    +
    Parameters
    +

    employee (Employee() Object) – Employee() object to assign.

    +
    +
    +
    + +
    + +
    +
    +

    File: Chop.py

    +

    This script contains the CHOP class used in the VRE model.

    +
    +
    +
    +class Chop.Chop(chop_code, chop_verwendungsjahr, chop, chop_code_level1, chop_level1, chop_code_level2, chop_level2, chop_code_level3, chop_level3, chop_code_level4, chop_level4, chop_code_level5, chop_level5, chop_code_level6, chop_level6, chop_status, chop_sap_katalog_id)
    +

    Models a CHOP code.

    +
    +
    +add_case(case)
    +

    Adds a case to the self.cases list.

    +
    +
    Parameters
    +

    case (Case() Object) – Case() object to append.

    +
    +
    +
    + +
    +
    +chop_code_stats(chops)
    +

    Print frequency of different chop codes to console.

    +
    +
    Parameters
    +

    chops (dict) –

    Dictionary mapping the chopcode_katalogid entries to Chop() objects

    +

    \(\longrightarrow\) { 'Z39.61.10_11': Chop(), ... }

    +

    +
    +
    +
    + +
    +
    +static create_chop_dict(lines)
    +

    Creates and returns a dict of all chop codes.

    +

    The key of a chop code is <code>_<catalog> - different catalogs exist for different years. This function +will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying +table in Atelier_DataScience is called V_DH_REF_CHOP and structured as follows:

    + +++++++++++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    CHOPCODE

    CHOPVERWENDUNGSJAHR

    CHOP

    CHOPCODELEVEL1

    CHOPLEVEL1

    CHOPCODELEVEL2

    CHOPLEVEL2

    CHOPCODELEVEL3

    CHOPLEVEL3

    CHOPCODELEVEL4

    CHOPLEVEL4

    CHOPCODELEVEL5

    CHOPLEVEL5

    CHOPCODELEVEL6

    CHOPLEVEL6

    CHOPSTATUS

    CHOPSAPKATALOGID

    Z62.99.30

    2016

    Entnahme von Hoden- oder Neben…

    C11

    Operationen an den männlichen Geschlechtsorganen (60–64)

    Z62

    Operationen an den Hoden

    Z62.9

    Sonstige Operationen an den Hoden

    Z62.99

    Sonstige Operationen an den Hoden

    Z62.99.0

    Detail der Subkategorie 62.99

    Z62.99.30

    Entnahme von Hoden- oder Nebenh…

    0

    16

    Z62.99.99

    2011

    Sonst. Operationen an den Ho…

    C9

    Operationen am Verdauungstrakt (42–54)

    Z62

    Operationen an den Hoden

    Z62.9

    Sonstige Operationen an den Hoden

    Z62.99

    Sonstige Operationen an den Hoden

    Z62.99.99

    Sonstige Operationen an den Hoden…

    1

    10

    +
    +
    Parameters
    +

    lines (iterator() object) – csv iterator from which data will be read

    +
    +
    Returns
    +

    Dictionary mapping the chopcode_katalogid entries to Chop() objects

    +

    \(\longrightarrow\) { 'Z39.61.10_11': Chop(), ... }

    +

    +
    +
    Return type
    +

    dict

    +
    +
    +
    + +
    +
    +get_detailed_chop()
    +

    Returns description text from the highest available level for this CHOP.

    +
    +
    Returns
    +

    Highest available level for this CHOP code.

    +
    +
    Return type
    +

    str

    +
    +
    +
    + +
    +
    +get_lowest_level_code()
    +

    Returns the lowest level of the CHOP code.

    +

    The lowest level is is level 2 and represented as the first number of the Code:

    +

    Z89.07.24 \(\longrightarrow\) Z89

    +
    +
    Returns
    +

    Lowest available level for this CHOP code.

    +
    +
    Return type
    +

    str

    +
    +
    +
    + +
    + +
    +
    +

    File: Devices.py

    +

    This script contains the Device class used in the VRE model.

    +
    +
    +
    +class Device.Device(geraet_id, geraet_name)
    +

    Models a device from RAP.

    +
    +
    +static add_device_to_appointment(lines, appointments, devices)
    +

    Adds the device in devices to the respective appointment in appointments.

    +

    This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). +The underlying table in the Atelier_DataScience is called V_DH_FACT_TERMINGERAET and structured as follows:

    + +++++++ + + + + + + + + + + + + + + + + + + + + + + +

    TERMINID

    GERAETID

    TERMINSTART_TS

    TERMINENDE_TS

    DAUERINMIN

    26266554

    123223

    2015-04-03 13:45:00.0000

    2015-04-03 15:45:00.0000

    120.000000

    23678836

    38006

    2014-07-31 10:00:00.0000

    2014-07-31 10:30:00.0000

    30.000000

    +
    +
    Parameters
    +
      +

    • lines (iterator() object) – csv iterator from which data will be read

      • +

    • appointments (dict) –

      Dictionary mapping appointment ids to Appointment() objects

      +

      \(\longrightarrow\) { '36830543' : Appointment(), ... }

      +

      • +

    • devices (dict) –

      Dictionary mapping device_ids to Device() objects

      +

      \(\longrightarrow\) {'64174' : Device(), ... }

      +

      • +
    +
    +
    +
    + +
    +
    +static create_device_map(lines)
    +

    Loads all devices into a dictionary based on lines in the csv file.

    +

    This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). +The underlying table in the Atelier_DataScience is called V_DH_DIM_GERAET_CUR and structured as follows:

    + ++++ + + + + + + + + + + + + + +

    GERAETID

    GERAETNAME

    82250

    ANS-Fix

    162101

    Waage

    +
    +
    Parameters
    +

    lines (iterator() object) – csv iterator from which data will be read

    +
    +
    Returns
    +

    Dictionary mapping device ids to Device() objects

    +

    \(\longrightarrow\) {'64174' : Device(), ... }

    +

    +
    +
    Return type
    +

    dict

    +
    +
    +
    + +
    + +
    +
    +

    File: Employee.py

    +

    This script contains the Employee class used in the VRE model.

    +
    +
    +
    +class Employee.Employee(mitarbeiter_id)
    +

    Models an employee (doctor, nurse, etc) from RAP.

    +
    +
    +static add_employee_to_appointment(lines, appointments, employees)
    +

    Adds Employee() in employees to an Appointment().

    +

    This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). +The underlying table is V_DH_FACT_TERMINMITARBEITER, and is identical to the one defined in the +create_employee_map() method above.

    +
    +
    Parameters
    +
      +

    • lines (iterator() object) – csv iterator from which data will be read

      • +

    • appointments (dict) –

      Dictionary mapping appointment ids to Appointment() objects

      +

      \(\longrightarrow\) { '36830543' : Appointment(), ... }

      +

      • +

    • employees (dict) –

      Dictionary mapping employee_ids to Employee() objects

      +

      \(\longrightarrow\) {'0032719' : Employee(), ... }

      +

      • +
    +
    +
    +
    + +
    +
    +static create_employee_map(lines)
    +

    Reads the appointment to employee file and creates an Employee().

    +

    This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). +The underlying table in the Atelier_DataScience is called V_DH_FACT_TERMINMITARBEITER and structured as follows:

    + +++++++ + + + + + + + + + + + + + + + + + + + + + + +

    TERMINID

    MITARBEITERID

    TERMINSTART_TS

    TERMINENDE_TS

    DAUERINMIN

    521664

    0063239

    2003-11-11 07:30:00.000

    2003-11-11 08:00:00.0000

    30.000000

    521754

    X33671

    2003-11-10 09:15:00.000

    2003-11-10 09:45:00.0000

    30.000000

    +
    +
    Parameters
    +

    lines (iterator() object) – csv iterator from which data will be read

    +
    +
    Returns
    +

    Dictionary mapping employee_ids to Employee() objects

    +

    \(\longrightarrow\) {'0032719' : Employee(), ... }

    +

    +
    +
    Return type
    +

    dict

    +
    +
    +
    + +
    + +
    +
    +

    File: ICD.py

    +

    This script contains the ICD class used in the VRE model.

    +
    +
    +
    +class ICD.ICD(falnr, dkey1, dkat, diadt, drg_cat)
    +

    Models an ICD object.

    +
    +
    +add_case(case)
    +

    Adds a case to this ICD’s self.cases attribute.

    +
    +
    Parameters
    +

    case (Case() Object) – Case() object to add.

    +
    +
    +
    + +
    +
    +static add_icd_to_case(lines, cases)
    +

    Adds ICD codes to cases based on the ICD.fall_nummer attribute.

    +

    For details on how each line in the lines iterator object is formatted, please refer to the function +create_icd_dict() above.

    +
    +
    Parameters
    +
      +

    • lines (iterator() object) – csv iterator from which data will be read

      • +

    • cases (dict) – Dictionary mapping case ids to Case() objects +\(\longrightarrow\) {"0003536421" : Case(), "0003473241" : Case(), ...}

      • +
    +
    +
    +
    + +
    +
    +static create_icd_dict(lines)
    +

    Creates and returns a dictionary of all icd codes.

    +

    This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). +The underlying table in the Atelier_DataScience is LA_ISH_NDIA and structured as follows:

    + +++++++ + + + + + + + + + + + + + + + + + + + + + + +

    ‘FALNR’

    ‘DKEY1’

    ‘DKAT1’

    ‘DIADT’

    ‘DRG_CATEGORY’

    ‘0001832682’

    ‘A41.9’

    ‘17’

    ‘2018-02-27’

    ‘P’

    ‘0001832682’

    ‘R65.1’

    ‘17’

    ‘2018-02-27’

    ‘S’

    +
    +
    Parameters
    +

    lines (iterator() object) – csv iterator from which data will be read

    +
    +
    Returns
    +

    Dictionary mapping the icd_code entries to ICD() objects

    +

    \(\longrightarrow\) { 'Z12.8': ICD(), ... }

    +

    +
    +
    Return type
    +

    dict

    +
    +
    +
    + +
    + +
    +
    +

    File: Medication.py

    +

    This script contains the Medication class used in the VRE model.

    +
    +
    +
    +class Medication.Medication(patient_id, case_id, drug_text, drug_atc, drug_quantity, drug_unit, drug_dispform, drug_submission)
    +

    Models a Medication object.

    +
    +
    +static add_medication_to_case(lines, cases)
    +

    Adds Medication() objects to Case() objects.

    +

    This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object), and +will add all Medication() objects to their corresponding Case() object in cases. The underlying table is +identical to the one used in the create_drug_map function.

    +
    +
    Parameters
    +
      +

    • lines (iterator() object) – csv iterator from which data will be read

      • +

    • cases (dict) –

      Dictionary mapping case ids to Case()

      +

      \(\longrightarrow\) {'0005976205' : Case(), ... }

      +

      • +
    +
    +
    +
    + +
    +
    +static create_drug_map(lines)
    +

    Creates a dictionary of ATC codes to human readable drug names.

    +

    This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). +The underlying table in the Atelier_DataScience is called V_LA_IPD_DRUG_NORM and structured as follows:

    + ++++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    PATIENTID

    CASEID

    DRUG_TEXT

    DRUG_ATC

    DRUG_QUANTITY

    DRUG_UNIT

    DRUG_DISPFORM

    DRUG_SUBMISSION

    00001711342

    0006437617

    Torem Tabl 10 mg (Torasemid)

    C03CA04

    2.0000000000000

    Stk

    p.o.

    2018-03-24 09:52:28.0000000

    00001711342

    0006437617

    Ecofenac Sandoz Lipogel 1 % (Diclofenac)

    M02AA15

    1.0000000000000

    Dos

    lokal / topisch

    2018-03-24 09:52:28.0000000

    +
    +
    Parameters
    +

    lines (iterator() object) – csv iterator from which data will be read

    +
    +
    Returns
    +

    Dictionary mapping drug codes to their respective text description

    +

    \(\longrightarrow\) {'B02BA01' : 'NaCl Braun Inf Lös 0.9 % 500 ml (Natriumchlorid)', ... }

    +

    +
    +
    Return type
    +

    dict

    +
    +
    +
    + +
    +
    +is_antibiotic()
    +

    Returns the antibiotic status of a Medication.

    +

    Antibiotics are identified via the prefix J01.

    +
    +
    Returns
    +

    Whether or not the medication self.drug_atc attribute starts with J01.

    +
    +
    Return type
    +

    bool

    +
    +
    +
    + +
    + +
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    +
    +add_device(device)
    +

    Adds a device to the self.devices() list of this appointment.

    +
    +
    Parameters
    +

    device (Device() Object) – Device() object to append to this appointment.

    +
    +
    +
    + +
    +
    +add_employee(employee)
    +

    Adds an employee to the self.employees() list of this appointment.

    +
    +
    Parameters
    +

    employee (Employee() Object) – Employee() object to append to this appointment.

    +
    +
    +
    + +
    +
    +add_room(room)
    +

    Adds a room to the self.rooms() list of this appointment.

    +
    +
    Parameters
    +

    room (Room() Object) – Room() object to append to this appointment.

    +
    +
    +
    + +
    +
    +static create_termin_map(lines)
    +

    Loads the appointments from a csv reader instance.

    +

    This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). +The underlying table in the Atelier_DataScience is named V_DH_DIM_TERMIN_CUR and structured as follows:

    + +++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    TERMINID

    IS_DELETED

    TERMINBEZEICHNUNG

    TERMINART

    TERMINTYP

    TERMINDATUM

    DAUERINMIN

    957219

    0

    K90 HINF

    K90 HINF

    Patiententermin

    2005-02-04 00:00:00.0000

    90.00000

    957224

    0

    Konsultation 15’

    Konsultation 15’

    Patiententermin

    2005-02-03 00:00:00.0000

    15.00000

    +
    +
    Parameters
    +

    lines (iterator() object) – csv iterator from which data will be read

    +
    +
    Returns
    +

    Dictionary mapping appointment ids to Appointment() objects

    +

    –> { '36830543' : Appointment(), ... }

    +

    +
    +
    Return type
    +

    dict

    +
    +
    +
    + +
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b/spitalhygiene/Docs/build/html/.buildinfo new file mode 100644 index 0000000..adf09cb --- /dev/null +++ b/spitalhygiene/Docs/build/html/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: 1800654d7c15071b98f1472f8661f81b +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/spitalhygiene/Docs/build/html/.nojekyll b/spitalhygiene/Docs/build/html/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/spitalhygiene/Docs/build/html/Patient_Test_Data.html b/spitalhygiene/Docs/build/html/Patient_Test_Data.html new file mode 100644 index 0000000..c88da4b --- /dev/null +++ b/spitalhygiene/Docs/build/html/Patient_Test_Data.html @@ -0,0 +1,546 @@ + + + + + + + + + + + Patient Test Data — VRE Model 0.0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    + + + +
    + + + + + +
    + +
    + + + + + + + + + + + + + + + + + +
    + + + + +
    +
    +
    +
    + +
    +

    Patient Test Data

    +

    The test patient dataset for the VRE model consists of a small subset of the complete dataset. The SQL queries used to +extract the test patient data are identical to those used for the true dataset, with the exception that data are +restricted to a small subset in the form of a simple WHERE statement. This filter includes the following 3 patient +IDs:

    +
      +

    • 00003067149

      • +

    • 00008301433

      • +

    • 00004348346

      • +
    +

    Associated with these patients are the following 40 case IDs:

    + ++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    Patient ID

    Case ID

    00003067149

    0006280483

    00003067149

    0006314210

    00003067149

    0006336438

    00003067149

    0005889802

    00003067149

    0005873082

    00003067149

    0006065973

    00003067149

    0006091736

    00003067149

    0006148746

    00003067149

    0006334066

    00003067149

    0006059391

    00003067149

    0005976205

    00003067149

    0006057834

    00003067149

    0005983693

    00003067149

    0006520444

    00003067149

    0006931777

    00003067149

    0006812114

    00003067149

    0005965462

    00003067149

    0006452545

    00003067149

    0006433446

    00003067149

    0006466165

    00004348346

    0001927876

    00004348346

    0004555507

    00004348346

    0004728517

    00004348346

    0001928507

    00004348346

    0002802610

    00004348346

    0004204668

    00004348346

    0004181978

    00003067149

    0006951942

    00003067149

    0005880782

    00008301433

    0002289902

    00008301433

    0004411153

    00008301433

    0004411005

    00008301433

    0006565152

    00008301433

    0003962974

    00008301433

    0006594482

    00008301433

    0006596375

    00008301433

    0006551728

    00003067149

    0005864325

    00003067149

    0005877026

    00003067149

    0006069476

    +

    For partners, only a single entry associated to one of the three test patients is used:

    +
      +

    • 0010000990

      • +
    +

    For CHOP codes, the following sub-selection of codes will be imported:

    +
      +

    • Z99.B8.11

      • +

    • Z50.27.32

      • +

    • Z00.99.60

      • +

    • Z88.38.60

      • +

    • Z99.85

      • +

    • Z89.07.24

      • +

    • Z50.27.32

      • +

    • Z00.99.60

      • +

    • Z88.38.60

      • +

    • Z99.85

      • +

    • Z50.23.13

      • +

    • Z50.12.09

      • +

    • Z88.79.50

      • +

    • Z00.9A.13

      • +

    • Z39.32.41

      • +

    • Z50.93

      • +

    • Z34.84

      • +

    • Z34.89.99

      • +

    • Z39.29.89

      • +

    • Z50.52

      • +

    • Z00.93.99

      • +

    • Z00.90.99

      • +

    • Z00.99.10

      • +

    • Z50.27.32

      • +

    • Z00.99.60

      • +

    • Z88.38.60

      • +

    • Z99.85

      • +

    • Z99.04.10

      • +

    • Z94.8X.40

      • +

    • Z99.B7.12

      • +

    • Z99.07.3C

      • +

    • Z99.04.15

      • +

    • Z99.05.47

      • +

    • Z99.B7.13

      • +

    • Z99.0A

      • +

    • Z99.28.11

      • +

    • Z50.52

      • +

    • Z54.52

      • +

    • Z00.93.99

      • +

    • Z00.90.99

      • +

    • Z51.22.11

      • +

    • Z39.29.89

      • +

    • Z99.00

      • +

    • Z54.12.11

      • +

    • Z50.12.12

      • +

    • Z88.79.50

      • +

    • Z54.25

      • +

    • Z36.11.22

      • +

    • Z36.11.26

      • +

    • Z36.1C.12

      • +

    • Z39.61.10

      • +

    • Z39.63

      • +

    • Z39.64

      • +

    • Z88.79.50

      • +

    • Z01.16.12

      • +

    • Z99.00

      • +
    +

    For appointments (german: “Termine”), only the following 98 TerminIDs are used:

    +
      +

    • 38515699

      • +

    • 38321122

      • +

    • 35416924

      • +

    • 1164130

      • +

    • 38470639

      • +

    • 41827160

      • +

    • 39893063

      • +

    • 38411180

      • +

    • 35571391

      • +

    • 35130813

      • +

    • 36160483

      • +

    • 40766840

      • +

    • 42155710

      • +

    • 39491988

      • +

    • 36067632

      • +

    • 37374631

      • +

    • 36129549

      • +

    • 39001478

      • +

    • 39425469

      • +

    • 34338471

      • +

    • 35630084

      • +

    • 35139096

      • +

    • 38431954

      • +

    • 38452040

      • +

    • 40344805

      • +

    • 13831398

      • +

    • 38063644

      • +

    • 38539785

      • +

    • 34220024

      • +

    • 39819467

      • +

    • 39423020

      • +

    • 38386995

      • +

    • 42394432

      • +

    • 38446243

      • +

    • 42213628

      • +

    • 38565198

      • +

    • 39893320

      • +

    • 37244357

      • +

    • 37554138

      • +

    • 41124954

      • +

    • 39051017

      • +

    • 36129560

      • +

    • 35621237

      • +

    • 38772701

      • +

    • 21130116

      • +

    • 38063650

      • +

    • 39608858

      • +

    • 39427731

      • +

    • 21131159

      • +

    • 38331618

      • +

    • 38062724

      • +

    • 24171386

      • +

    • 14908956

      • +

    • 41909560

      • +

    • 39114133

      • +

    • 14091256

      • +

    • 38939623

      • +

    • 35626775

      • +

    • 35139491

      • +

    • 36006751

      • +

    • 38329080

      • +

    • 41909690

      • +

    • 35130747

      • +

    • 36129541

      • +

    • 1278803

      • +

    • 38507433

      • +

    • 1192059

      • +

    • 39456191

      • +

    • 14091249

      • +

    • 39933520

      • +

    • 24291359

      • +

    • 36071093

      • +

    • 36160474

      • +

    • 19096210

      • +

    • 40218521

      • +

    • 1162144

      • +

    • 38660148

      • +

    • 42211133

      • +

    • 39613790

      • +

    • 24230235

      • +

    • 38262758

      • +

    • 35417252

      • +

    • 19252406

      • +

    • 39215737

      • +

    • 38446041

      • +

    • 36830543

      • +

    • 35200182

      • +

    • 40766156

      • +

    • 36070942

      • +

    • 34310589

      • +

    • 37232112

      • +

    • 34337667

      • +

    • 38446523

      • +

    • 34482529

      • +

    • 17297480

      • +

    • 39298995

      • +

    • 36830574

      • +

    • 1405150

      • +
    +

    And finally for devices, the subset is restricted to the following GeraetIDs:

    +
      +

    • 134074

      • +

    • 125922

      • +

    • 137160

      • +

    • 125916

      • +

    • 125981

      • +

    • 125981

      • +

    • 64174

      • +

    • 125921

      • +

    • 125981

      • +

    • 125981

      • +

    • 125981

      • +

    • 125981

      • +

    • 125981

      • +

    • 28609

      • +

    • 86293

      • +

    • 125981

      • +

    • 125981

      • +

    • 125981

      • +

    • 86293

      • +

    • 125981

      • +

    • 125981

      • +

    • 64174

      • +

    • 125981

      • +

    • 125981

      • +

    • 125981

      • +

    • 125981

      • +

    • 125981

      • +

    • 125974

      • +

    • 28609

      • +

    • 125981

      • +
    +
    +
    + + +
    + +
    + + +
    +
    + +
    + +
    + + + + + + + + + + + + \ No newline at end of file diff --git a/spitalhygiene/Docs/build/html/VRE_Model.html b/spitalhygiene/Docs/build/html/VRE_Model.html new file mode 100644 index 0000000..60cc52d --- /dev/null +++ b/spitalhygiene/Docs/build/html/VRE_Model.html @@ -0,0 +1,211 @@ + + + + + + + + + + + VRE Model Overview — VRE Model 0.0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
    + + + + + +
    + +
    + + + + + + + + + + + + + + + + + +
    + + + + +
    +
    +
    +
    + +
    +

    VRE Model Overview

    +

    Welcome to the VRE Model Documentation!

    +

    The VRE model is documented with Sphinx on a per-folder basis. The root folder for this documentation is the +spitalhygiene folder of the insel mono-repo on BitBucket.

    +
    + + +
    + +
    + + +
    +
    + +
    + +
    + + + + + + + + + + + + \ No newline at end of file diff --git a/spitalhygiene/Docs/build/html/_sources/Patient_Test_Data.rst.txt b/spitalhygiene/Docs/build/html/_sources/Patient_Test_Data.rst.txt new file mode 100644 index 0000000..1bb2794 --- /dev/null +++ b/spitalhygiene/Docs/build/html/_sources/Patient_Test_Data.rst.txt @@ -0,0 +1,258 @@ +******************** +Patient Test Data +******************** + +The test patient dataset for the VRE model consists of a small subset of the complete dataset. The SQL queries used to +extract the test patient data are `identical` to those used for the true dataset, with the exception that data are +restricted to a small subset in the form of a simple ``WHERE`` statement. This filter includes the following 3 patient +IDs: + +- 00003067149 +- 00008301433 +- 00004348346 + +Associated with these patients are the following 40 case IDs: + +=========== =========== +Patient ID Case ID +=========== =========== +00003067149 0006280483 +00003067149 0006314210 +00003067149 0006336438 +00003067149 0005889802 +00003067149 0005873082 +00003067149 0006065973 +00003067149 0006091736 +00003067149 0006148746 +00003067149 0006334066 +00003067149 0006059391 +00003067149 0005976205 +00003067149 0006057834 +00003067149 0005983693 +00003067149 0006520444 +00003067149 0006931777 +00003067149 0006812114 +00003067149 0005965462 +00003067149 0006452545 +00003067149 0006433446 +00003067149 0006466165 +00004348346 0001927876 +00004348346 0004555507 +00004348346 0004728517 +00004348346 0001928507 +00004348346 0002802610 +00004348346 0004204668 +00004348346 0004181978 +00003067149 0006951942 +00003067149 0005880782 +00008301433 0002289902 +00008301433 0004411153 +00008301433 0004411005 +00008301433 0006565152 +00008301433 0003962974 +00008301433 0006594482 +00008301433 0006596375 +00008301433 0006551728 +00003067149 0005864325 +00003067149 0005877026 +00003067149 0006069476 +=========== =========== + +For partners, only a single entry associated to one of the three test patients is used: + +- 0010000990 + +For CHOP codes, the following sub-selection of codes will be imported: + +- Z99.B8.11 +- Z50.27.32 +- Z00.99.60 +- Z88.38.60 +- Z99.85 +- Z89.07.24 +- Z50.27.32 +- Z00.99.60 +- Z88.38.60 +- Z99.85 +- Z50.23.13 +- Z50.12.09 +- Z88.79.50 +- Z00.9A.13 +- Z39.32.41 +- Z50.93 +- Z34.84 +- Z34.89.99 +- Z39.29.89 +- Z50.52 +- Z00.93.99 +- Z00.90.99 +- Z00.99.10 +- Z50.27.32 +- Z00.99.60 +- Z88.38.60 +- Z99.85 +- Z99.04.10 +- Z94.8X.40 +- Z99.B7.12 +- Z99.07.3C +- Z99.04.15 +- Z99.05.47 +- Z99.B7.13 +- Z99.0A +- Z99.28.11 +- Z50.52 +- Z54.52 +- Z00.93.99 +- Z00.90.99 +- Z51.22.11 +- Z39.29.89 +- Z99.00 +- Z54.12.11 +- Z50.12.12 +- Z88.79.50 +- Z54.25 +- Z36.11.22 +- Z36.11.26 +- Z36.1C.12 +- Z39.61.10 +- Z39.63 +- Z39.64 +- Z88.79.50 +- Z01.16.12 +- Z99.00 + +For appointments (german: "Termine"), only the following 98 TerminIDs are used: + +- 38515699 +- 38321122 +- 35416924 +- 1164130 +- 38470639 +- 41827160 +- 39893063 +- 38411180 +- 35571391 +- 35130813 +- 36160483 +- 40766840 +- 42155710 +- 39491988 +- 36067632 +- 37374631 +- 36129549 +- 39001478 +- 39425469 +- 34338471 +- 35630084 +- 35139096 +- 38431954 +- 38452040 +- 40344805 +- 13831398 +- 38063644 +- 38539785 +- 34220024 +- 39819467 +- 39423020 +- 38386995 +- 42394432 +- 38446243 +- 42213628 +- 38565198 +- 39893320 +- 37244357 +- 37554138 +- 41124954 +- 39051017 +- 36129560 +- 35621237 +- 38772701 +- 21130116 +- 38063650 +- 39608858 +- 39427731 +- 21131159 +- 38331618 +- 38062724 +- 24171386 +- 14908956 +- 41909560 +- 39114133 +- 14091256 +- 38939623 +- 35626775 +- 35139491 +- 36006751 +- 38329080 +- 41909690 +- 35130747 +- 36129541 +- 1278803 +- 38507433 +- 1192059 +- 39456191 +- 14091249 +- 39933520 +- 24291359 +- 36071093 +- 36160474 +- 19096210 +- 40218521 +- 1162144 +- 38660148 +- 42211133 +- 39613790 +- 24230235 +- 38262758 +- 35417252 +- 19252406 +- 39215737 +- 38446041 +- 36830543 +- 35200182 +- 40766156 +- 36070942 +- 34310589 +- 37232112 +- 34337667 +- 38446523 +- 34482529 +- 17297480 +- 39298995 +- 36830574 +- 1405150 + +And finally for devices, the subset is restricted to the following GeraetIDs: + +- 134074 +- 125922 +- 137160 +- 125916 +- 125981 +- 125981 +- 64174 +- 125921 +- 125981 +- 125981 +- 125981 +- 125981 +- 125981 +- 28609 +- 86293 +- 125981 +- 125981 +- 125981 +- 86293 +- 125981 +- 125981 +- 64174 +- 125981 +- 125981 +- 125981 +- 125981 +- 125981 +- 125974 +- 28609 +- 125981 + +------ diff --git a/spitalhygiene/Docs/build/html/_sources/VRE_Model.rst.txt b/spitalhygiene/Docs/build/html/_sources/VRE_Model.rst.txt new file mode 100644 index 0000000..669f4e5 --- /dev/null +++ b/spitalhygiene/Docs/build/html/_sources/VRE_Model.rst.txt @@ -0,0 +1,11 @@ +******************** +VRE Model Overview +******************** + +Welcome to the VRE Model Documentation! + +The VRE model is documented with `Sphinx` on a `per-folder` basis. The root folder for this documentation is the +``spitalhygiene`` folder of the ``insel`` mono-repo on BitBucket. + + + diff --git a/spitalhygiene/Docs/build/html/_sources/index.rst.txt b/spitalhygiene/Docs/build/html/_sources/index.rst.txt new file mode 100644 index 0000000..f06dcca --- /dev/null +++ b/spitalhygiene/Docs/build/html/_sources/index.rst.txt @@ -0,0 +1,25 @@ +.. VRE Model documentation master file, created by + sphinx-quickstart on Tue Apr 23 15:53:29 2019. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to the VRE Model documentation! +======================================= + +.. toctree:: + :caption: Contents: + + VRE_Model.rst + resources.rst + sql.rst + vre.rst + Patient_Test_Data.rst + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/spitalhygiene/Docs/build/html/_sources/resources.rst.txt b/spitalhygiene/Docs/build/html/_sources/resources.rst.txt new file mode 100644 index 0000000..86816d6 --- /dev/null +++ b/spitalhygiene/Docs/build/html/_sources/resources.rst.txt @@ -0,0 +1,21 @@ +************************ +``resources`` folder +************************ + +This folder contains important functions for loading data from SQL into CSV, thereby preparing the "raw" data used for +building the actual network models. + +Most importantly, this folder also contains the file ``Update_Model.sh``, which is a bash script controlling `all steps` +in the VRE calculation. Since all VRE data are recalculated once per day, this includes (in order): + +1) Backing up data from the previous calculation cycle in the HDFS file system (from step 4 in the previous run) +2) Reloading data from SQL into CSV +3) Running the VRE analysis (all steps of the analysis are controlled with ``feature_extractor.py``) +4) Adding new data (SQL files, CSV files, other data) to the HDFS file system + + +File: Query_Atelier_Data.py +=============================== + +.. automodule:: Query_Atelier_Data + :members: \ No newline at end of file diff --git a/spitalhygiene/Docs/build/html/_sources/sql.rst.txt b/spitalhygiene/Docs/build/html/_sources/sql.rst.txt new file mode 100644 index 0000000..4ccc191 --- /dev/null +++ b/spitalhygiene/Docs/build/html/_sources/sql.rst.txt @@ -0,0 +1,6 @@ +************************ +``sql`` folder +************************ + +This folder contains important functions for extracting SQL data. + diff --git a/spitalhygiene/Docs/build/html/_sources/vre.rst.txt b/spitalhygiene/Docs/build/html/_sources/vre.rst.txt new file mode 100644 index 0000000..15213bf --- /dev/null +++ b/spitalhygiene/Docs/build/html/_sources/vre.rst.txt @@ -0,0 +1,36 @@ +***************************************** +``vre/src/main/python/vre`` folder +***************************************** + +This folder contains all files relevant for building the actual VRE model. + + +----- + +File: data_compiler.py +=============================== + +.. automodule:: data_compiler + :members: + + +File: feature_extractor.py +=============================== + +.. automodule:: feature_extractor + :members: + + +File: HDFS_data_loader.py +=============================== + +.. automodule:: HDFS_data_loader + :members: + + +File: networkx_graph.py +=============================== + +.. automodule:: networkx_graph + :members: + diff --git a/spitalhygiene/Docs/build/html/_static/ajax-loader.gif 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without + * an installed firebug like debugger +if (!window.console || !console.firebug) { + var names = ["log", "debug", "info", "warn", "error", "assert", "dir", + "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", + "profile", "profileEnd"]; + window.console = {}; + for (var i = 0; i < names.length; ++i) + window.console[names[i]] = function() {}; +} + */ + +/** + * small helper function to urldecode strings + */ +jQuery.urldecode = function(x) { + return decodeURIComponent(x).replace(/\+/g, ' '); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} + +/** + * Small JavaScript module for the documentation. + */ +var Documentation = { + + init : function() { + this.fixFirefoxAnchorBug(); + this.highlightSearchWords(); + this.initIndexTable(); + if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { + this.initOnKeyListeners(); + } + }, + + /** + * i18n support + */ + TRANSLATIONS : {}, + PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, + LOCALE : 'unknown', + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext : function(string) { + var translated = Documentation.TRANSLATIONS[string]; + if (typeof translated === 'undefined') + return string; + return (typeof translated === 'string') ? translated : translated[0]; + }, + + ngettext : function(singular, plural, n) { + var translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated === 'undefined') + return (n == 1) ? singular : plural; + return translated[Documentation.PLURALEXPR(n)]; + }, + + addTranslations : function(catalog) { + for (var key in catalog.messages) + this.TRANSLATIONS[key] = catalog.messages[key]; + this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); + this.LOCALE = catalog.locale; + }, + + /** + * add context elements like header anchor links + */ + addContextElements : function() { + $('div[id] > :header:first').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this headline')). + appendTo(this); + }); + $('dt[id]').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this definition')). + appendTo(this); + }); + }, + + /** + * workaround a firefox stupidity + * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 + */ + fixFirefoxAnchorBug : function() { + if (document.location.hash && $.browser.mozilla) + window.setTimeout(function() { + document.location.href += ''; + }, 10); + }, + + /** + * highlight the search words provided in the url in the text + */ + highlightSearchWords : function() { + var params = $.getQueryParameters(); + var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; + if (terms.length) { + var body = $('div.body'); + if (!body.length) { + body = $('body'); + } + window.setTimeout(function() { + $.each(terms, function() { + body.highlightText(this.toLowerCase(), 'highlighted'); + }); + }, 10); + $('

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z8(cm%K|alp@wXnwJ`~j-9cJyjV!rHvNK{Y#k5akaZTo&(n{x2(MN!p0&Yll-G^t1} zrm{#X0`=Qp^`!E+efumW&p$U8|EIR^6frc~Q0)QZanCLDtKP!Bec0!%Os~1z!8@7_ zrWz?ZUus1JoFgI!QVaW13E01^mZ*_|x=y9Opb`?j2PSrEavR46Z(HtwPqz{%3P9ky z3Ss@tC;wI1&;J7F_U(_0hB{2m7485k@JQkiR=eTjXR=}7VuxA|U1X9?dOC6Y)t>L! zIO(}Y=y+FteW$WZl=`D^!ect7lQ3Uux^I?|QiT3S+r}ECZ-$J0bD#Ud8k`b^w4SFrkP0_v)YX0x(yTWyay- zO>EI#V=)yZa7L8mZ#}U4r^Nr8@Udx1m}XaXnD(@8nbpmZ+me5FU3h+)_f#byMY @@ -156,7 +157,7 @@

      VRE Model Overview

      Welcome to the VRE Model Documentation!

      The VRE model is documented with Sphinx on a per-folder basis. The root folder for this documentation is the -spitalhygiene folder of the insel mono-repo on BitBucket.

      +spitalhygiene folder of the vre-data-science repository on the Insel GitHub.

      diff --git a/spitalhygiene/Docs/build/html/_sources/Unused.rst.txt b/spitalhygiene/Docs/build/html/_sources/Unused.rst.txt new file mode 100644 index 0000000..bf77846 --- /dev/null +++ b/spitalhygiene/Docs/build/html/_sources/Unused.rst.txt @@ -0,0 +1,6 @@ +************************ +``Unused`` folder +************************ + +This folder contains archived files and scripts no longer used in the current version of the VRE model. + diff --git a/spitalhygiene/Docs/build/html/_sources/VRE_Model.rst.txt b/spitalhygiene/Docs/build/html/_sources/VRE_Model.rst.txt index 669f4e5..84f8a6b 100644 --- a/spitalhygiene/Docs/build/html/_sources/VRE_Model.rst.txt +++ b/spitalhygiene/Docs/build/html/_sources/VRE_Model.rst.txt @@ -5,7 +5,7 @@ VRE Model Overview Welcome to the VRE Model Documentation! The VRE model is documented with `Sphinx` on a `per-folder` basis. The root folder for this documentation is the -``spitalhygiene`` folder of the ``insel`` mono-repo on BitBucket. +``spitalhygiene`` folder of the ``vre-data-science`` repository on the Insel GitHub. diff --git a/spitalhygiene/Docs/build/html/_sources/index.rst.txt b/spitalhygiene/Docs/build/html/_sources/index.rst.txt index f06dcca..fa32ed8 100644 --- a/spitalhygiene/Docs/build/html/_sources/index.rst.txt +++ b/spitalhygiene/Docs/build/html/_sources/index.rst.txt @@ -12,14 +12,10 @@ Welcome to the VRE Model documentation! VRE_Model.rst resources.rst sql.rst + Unused.rst vre.rst + model.rst Patient_Test_Data.rst -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/spitalhygiene/Docs/build/html/_sources/model.rst.txt b/spitalhygiene/Docs/build/html/_sources/model.rst.txt new file mode 100644 index 0000000..0d52e1f --- /dev/null +++ b/spitalhygiene/Docs/build/html/_sources/model.rst.txt @@ -0,0 +1,65 @@ +***************************************** +``vre/src/main/python/vre/model`` folder +***************************************** + +This folder contains the definitions for all classes used in the VRE model. + + +----- + +File: Appointment.py +=============================== + +.. automodule:: Appointment + :members: + + +File: Bed.py +=============================== + +.. automodule:: Bed + :members: + + +File: Care.py +=============================== + +.. automodule:: Care + :members: + + +File: Chop.py +=============================== + +.. automodule:: Chop + :members: + + +File: Devices.py +=============================== + +.. automodule:: Device + :members: + + +File: Employee.py +=============================== + +.. automodule:: Employee + :members: + + +File: ICD.py +=============================== + +.. automodule:: ICD + :members: + + +File: Medication.py +=============================== + +.. automodule:: Medication + :members: + + diff --git a/spitalhygiene/Docs/build/html/_sources/resources.rst.txt b/spitalhygiene/Docs/build/html/_sources/resources.rst.txt index 86816d6..a0a0813 100644 --- a/spitalhygiene/Docs/build/html/_sources/resources.rst.txt +++ b/spitalhygiene/Docs/build/html/_sources/resources.rst.txt @@ -18,4 +18,11 @@ File: Query_Atelier_Data.py =============================== .. automodule:: Query_Atelier_Data - :members: \ No newline at end of file + :members: + + +File: preprocesor.py +=============================== + +.. automodule:: preprocessor + :members: diff --git a/spitalhygiene/Docs/build/html/genindex.html b/spitalhygiene/Docs/build/html/genindex.html index 96e9ec3..b0794c8 100644 --- a/spitalhygiene/Docs/build/html/genindex.html +++ b/spitalhygiene/Docs/build/html/genindex.html @@ -85,7 +85,9 @@
    1. VRE Model Overview
    2. resources folder
    3. sql folder
      4. +
    4. Unused folder
    5. vre/src/main/python/vre folder
      6. +
    6. vre/src/main/python/vre/model folder
    7. Patient Test Data
      8. @@ -154,6 +156,7 @@

      Index

      A + | B | C | D | E @@ -162,6 +165,7 @@

      Index

      | H | I | L + | M | N | P | Q @@ -175,19 +179,95 @@

      Index

      A

      +
      + +

      B

      + +

      C

      +
      @@ -198,19 +278,35 @@

      D

    8. data_compiler (module)
      9. +

      E

      + -
      @@ -263,6 +363,12 @@

      H

      I

      @@ -270,6 +376,8 @@

      I

    9. identify_node() (networkx_graph.surface_model method)
    10. inspect_network() (networkx_graph.surface_model method) +
      11. +
    11. is_antibiotic() (Medication.Medication method)
      12. @@ -282,6 +390,18 @@

      L

      +

      M

      + + +
      +

      N

      - +
        @@ -309,11 +429,13 @@

        P
      @@ -329,6 +451,16 @@

      Q

      R

      + diff --git a/spitalhygiene/Docs/build/html/index.html b/spitalhygiene/Docs/build/html/index.html index b55aeb0..65b6dd3 100644 --- a/spitalhygiene/Docs/build/html/index.html +++ b/spitalhygiene/Docs/build/html/index.html @@ -85,7 +85,9 @@
    12. VRE Model Overview
    13. resources folder
    14. sql folder
      15. +
    15. Unused folder
    16. vre/src/main/python/vre folder
      17. +
    17. vre/src/main/python/vre/model folder
    18. Patient Test Data
      19. @@ -159,9 +161,11 @@

      Welcome to the VRE Model documentation!VRE Model Overview
    19. resources folder
    20. sql folder
      21. +
    21. Unused folder
    22. vre/src/main/python/vre folder
      23. +
    23. vre/src/main/python/vre/model folder +
    24. Patient Test Data
      25. - -
      -

      Indices and tables

      -
      diff --git a/spitalhygiene/Docs/build/html/model.html b/spitalhygiene/Docs/build/html/model.html new file mode 100644 index 0000000..82a1041 --- /dev/null +++ b/spitalhygiene/Docs/build/html/model.html @@ -0,0 +1,1052 @@ + + + + + + + + + + + vre/src/main/python/vre/model folder — VRE Model 0.0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      + + + +
      + + + + + +
      + +
      + + + + + + + + + + + + + + + + + +
      + + + + +
      +
      +
      +
      + +
      +

      vre/src/main/python/vre/model folder

      +

      This folder contains the definitions for all classes used in the VRE model.

      +
      +
      +

      File: Appointment.py

      +

      This script contains the Appointment class used in the VRE model.

      +
      +
      +
      +class Appointment.Appointment(termin_id, is_deleted, termin_bezeichnung, termin_art, termin_typ, termin_datum, dauer_in_min)
      +

      Models an appointment from RAP.

      +
      +
      +static add_appointment_to_case(lines, cases, appointments)
      +

      Adds Appointment() objects to the SAP cases based on lines read from a csv file.

      +

      This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). +The underlying table in the Atelier_DataScience is called V_DH_FACT_TERMINPATIENT and structured as follows:

      +

      +++++ + + + + + + + + + + + + + + + + +

      TERMINID

      PATIENTID

      FALLID

      35672314

      00008210020

      0005660334

      17255155

      00002042800

      0004017880

      +
      +
      Parameters
      +
        +

      • lines (iterator() object) – csv iterator from which data will be read

        • +

      • cases (dict) –

        Dictionary mapping case ids to Case() objects

        +

        –> { "0003536421" : Case(), "0003473241" : Case(), ...}

        +

        • +

      • appointments (dict) –

        Dictionary mapping appointment ids to Appointment() objects

        +

        –> { '36830543' : Appointment(), ... }

        +

        • +
      +
      +
      +

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with open(path_to_csv, 'w') as writefile: + csv_writer = csv.writer(writefile, dialect = 'sql_special') + csv_writer.writerow([i[0] for i in cursor.description]) # write headers + csv_writer.writerows(cursor) + + # close connection + conn.close() + +if __name__ == '__main__': # Necessary to avoid code parsing by Sphinx + ################################################################################################################ + # Extract correct filepath + this_filepath = os.path.dirname(os.path.realpath(__file__)) + # contains the directory in which this script is located, irrespective of the current working directory + + # Load config file: + config_reader = configparser.ConfigParser() + config_reader.read(os.path.join(this_filepath, '../vre/src/main/python/vre/BasicConfig.ini')) + + SQL_DIR = os.path.join(this_filepath, "../sql/tests") if config_reader['PARAMETERS']['data_basis'] == 'test' \ + else os.path.join(this_filepath, "../sql/vre") # absolute or relative path to directory containing SQL files + + CSV_DIR = config_reader['PATHS']['test_data_dir'] if config_reader['PARAMETERS']['data_basis'] == 'test' \ + else config_reader['PATHS']['model_data_dir'] # absolute or relative path to directory where data is stored + + CSV_DELIM = config_reader['DELIMITERS']['csv_sep'] # delimiter for CSV files written from SQL results + + print(f"data_basis set to: {config_reader['PARAMETERS']['data_basis']}\n") + + ################################################################################################################ + # Execute all queries in SQL_DIR + print('Loading data from SQL server:\n') + + sql_files = [each_file for each_file in os.listdir(SQL_DIR) if each_file.endswith('.sql')] + # --> Use this line instead for loading only specific files: + # sql_files = [each_file for each_file in os.listdir(SQL_DIR) if each_file in ['deleted_screenings.sql']] + + for each_file in sql_files: + print('--> Loading file: ' + each_file + ' ... ', end='', flush=True) + start_dt = datetime.datetime.now() + + # Execute query and write results + WriteSQLToCSV(path_to_sql=os.path.join(SQL_DIR, each_file), + path_to_csv=os.path.join(CSV_DIR, each_file.replace('.sql', '.csv')), csv_sep=CSV_DELIM, + connection_file=config_reader['PATHS']['odbc_file_path'], trusted_connection=False) + + print(f'\tDone !\t Total processing time: {str(datetime.datetime.now()-start_dt).split(".")[0]}') + # --> print timedelta without fractional seconds (original string would be printed as 0:00:13.4567) + + ##################################### + print('\nAll files loaded successfully !') + ##################################### + + diff --git a/spitalhygiene/resources/Update_Model.sh b/spitalhygiene/resources/Update_Model.sh new file mode 100644 index 0000000..b217663 --- /dev/null +++ b/spitalhygiene/resources/Update_Model.sh @@ -0,0 +1,102 @@ +#!/bin/bash -e + +### Start time measurement +START_TIME=$SECONDS + +### Activate virtual environment +source /home/i0308559/vre_venv/bin/activate + +########################################################################################################################### +### VARIABLE DECLARATION +########################################################################################################################### + +MODEL_DIR=/home/i0308559/vre_input +NEO4J_DIR=/home/i0308559/vre_output/neo4j +FEATVEC_DIR=/home/i0308559/vre_output/feature_vector +LOG_PATH=/home/i0308559/vre_log + +########################################################################################################################### +### FILE BACKUP (ONLY PERFORMED IN HDFS) +########################################################################################################################### + +### Backup HDFS files from the SQL export (stored in MODEL_DIR) +echo "backing up HDFS files for model data" +# sudo -u hdfs hdfs dfs -rm -r ${MODEL_DIR}/old +# sudo -u hdfs hdfs dfs -mkdir ${MODEL_DIR}/old +# sudo -u hdfs hdfs dfs -mv ${MODEL_DIR}/*.csv ${MODEL_DIR}/old +echo "Done !" + +### Backup HDFS files from the Neo4J export +echo "backing up HDFS files for Neo4J data" +# sudo -u hdfs hdfs dfs -rm -r ${NEO4J_DIR}/old +# sudo -u hdfs hdfs dfs -mkdir ${NEO4J_DIR}/old +# sudo -u hdfs hdfs dfs -mv ${NEO4J_DIR}/*.csv ${NEO4J_DIR}/old +echo "Done !" + +### Backup the feature vector +echo "backing up the feature vector" +# sudo -u hdfs hdfs dfs -rm -r ${FEATVEC_DIR}/old +# sudo -u hdfs hdfs dfs -mkdir ${FEATVEC_DIR}/old +# sudo -u hdfs hdfs dfs -mv ${FEATVEC_DIR}/*.csv ${FEATVEC_DIR}/old +echo "Done !" + +########################################################################################################################### +### MODEL DATA EXTRACTION +########################################################################################################################### + +### Extract data from the SQL server into CSV (must be called by the virtual environment interpreter to load pyodbc module) +echo "Extracting new data from the Atelier_DataScience into CSV..." +python3.6 /home/i0308559/spitalhygiene/resources/Query_Atelier_Data.py > ${LOG_PATH}/SQL_Data_Load.log 2>&1 # pyodbc module installed +# python3.6 /home/i0308559/spitalhygiene/resources/update_db_jar.py > ${LOG_PATH}/SQL_Data_Load.log 2>&1 # pyodbc module NOT installed +echo "Done !" + +########################################################################################################################### +### MODEL CALCULATION +########################################################################################################################### + +### Run the feature_extractor.py script, which performs +# --> data load from the CSV files produced by the SQL query +# --> creation and export of the feature vector into FEATVEC_DIR +# --> Export of data in Neo4J-compatible format into NEO4J_DIR +# --> Calculation of the statistical model (random forests???) and its desired output (NOT DESIGNED YET - WHAT SHOULD WE PRODUCE?) +echo "Running feature_extractor.py..." +python3.6 /home/i0308559/spitalhygiene/vre/src/main/python/vre/data_compiler.py > ${LOG_PATH}/Compiler.log 2>&1 +echo "Done !" + +########################################################################################################################### +### TRANSFER TO HDFS +########################################################################################################################### + +### Add extracted CSV files to HDFS +echo "Putting files to HDFS:" +printf ">> SQL Query files... " +# sudo -u hdfs hdfs dfs -put ${MODEL_DIR}/*.csv /data1/sqooba/vre/model_data/ +echo "Done !" + +printf ">> Feature vector... " +# sudo -u hdfs hdfs dfs -put ${FEATVEC_DIR}/*.csv /data1/sqooba/vre/data_export/feature_vector +echo "Done !" + +printf ">> Neo4J files... " +# sudo -u hdfs hdfs dfs -put ${NEO4J_DIR}/*.csv /data1/sqooba/vre/data_export/neo4j +echo "Done !" + +########################################################################################################################### +### PROCESS MODEL OUTPUT (NOT YET USED) +########################################################################################################################### +# This part includes any additional processing steps (e.g. sending of a name list by email for screening candidates, etc) +# +# This part (i.e. the model PURPOSE) has not yet been determined. + +########################################################################################################################### +### PRINT SUCCESS AND EXECUTION TIME TO LOGFILE +########################################################################################################################### +ELAPSED_TIME=$(($SECONDS - $START_TIME)) +HOURS=$(($ELAPSED_TIME/3600)) +MINUTES=$((($ELAPSED_TIME-$HOURS*3600)/60)) +SECONDS=$((($ELAPSED_TIME-$HOURS*3600)%60)) + +echo "" +echo "Model updated successfully !" +echo "Update duration: $HOURS h, $MINUTES minutes, $SECONDS seconds" + diff --git a/spitalhygiene/resources/query_device.sql b/spitalhygiene/resources/query_device.sql new file mode 100644 index 0000000..4627707 --- /dev/null +++ b/spitalhygiene/resources/query_device.sql @@ -0,0 +1,20 @@ +SELECT * FROM [Atelier_DataScience].[atl].[V_DH_DIM_MITARBEITER_CUR] WHERE MITARBEITERID in +(SELECT distinct MITARBEITERID from [Atelier_DataScience].[atl].[V_DH_FACT_TERMINMITARBEITER] WHERE TERMINID in + (SELECT TERMINID FROM [Atelier_DataScience].[atl].[V_DH_FACT_TERMINGERAET] + WHERE GERAETID IN + (SELECT GERAETID + FROM [Atelier_DataScience].[atl].[V_DH_DIM_GERAET_CUR] + WHERE GERAETNAME ='ECC') + AND TERMINSTART_TS > '2018-01-01') +) + + +SELECT * FROM [Atelier_DataScience].[atl].[V_DH_DIM_RAUM_CUR] WHERE RAUMID in +(SELECT distinct RAUMID from [Atelier_DataScience].[atl].[V_DH_FACT_TERMINRAUM] WHERE TERMINID in + (SELECT TERMINID FROM [Atelier_DataScience].[atl].[V_DH_FACT_TERMINGERAET] + WHERE GERAETID IN + (SELECT GERAETID + FROM [Atelier_DataScience].[atl].[V_DH_DIM_GERAET_CUR] + WHERE GERAETNAME ='ECC') + AND TERMINSTART_TS > '2018-01-01') +) \ No newline at end of file diff --git a/spitalhygiene/resources/tree_all.html b/spitalhygiene/resources/tree_all.html new file mode 100644 index 0000000..cb0bf81 --- /dev/null +++ b/spitalhygiene/resources/tree_all.html @@ -0,0 +1,169 @@ + + + + + + \ No newline at end of file diff --git a/spitalhygiene/resources/tree_devices.html b/spitalhygiene/resources/tree_devices.html new file mode 100644 index 0000000..c66b113 --- /dev/null +++ b/spitalhygiene/resources/tree_devices.html @@ -0,0 +1,169 @@ + + + + + + \ No newline at end of file diff --git a/spitalhygiene/resources/tree_employees.html b/spitalhygiene/resources/tree_employees.html new file mode 100644 index 0000000..8cde4aa --- /dev/null +++ b/spitalhygiene/resources/tree_employees.html @@ -0,0 +1,169 @@ + + + + + + \ No newline at end of file diff --git a/spitalhygiene/resources/tree_nomed.html b/spitalhygiene/resources/tree_nomed.html new file mode 100644 index 0000000..744b437 --- /dev/null +++ b/spitalhygiene/resources/tree_nomed.html @@ -0,0 +1,169 @@ + + + + + + \ No newline at end of file diff --git a/spitalhygiene/resources/tree_rooms.html b/spitalhygiene/resources/tree_rooms.html new file mode 100644 index 0000000..df1d056 --- /dev/null +++ b/spitalhygiene/resources/tree_rooms.html @@ -0,0 +1,169 @@ + + + + + + \ No newline at end of file diff --git a/spitalhygiene/resources/umgebungsabklaerung.cypher b/spitalhygiene/resources/umgebungsabklaerung.cypher new file mode 100644 index 0000000..c0976c7 --- /dev/null +++ b/spitalhygiene/resources/umgebungsabklaerung.cypher @@ -0,0 +1,10 @@ +MATCH (n:Patient{PATNR:'00000532290'})-[k]-(m:Patient) +WHERE k.from > n.risk_datetime - duration('P8D') +AND n.PATNR <> m.PATNR +RETURN n.PATNR, + m.PATNR, + k.from AS von, + k.to AS bis, + k.room AS ort, + CASE type(k) WHEN 'kontakt_raum' THEN 'Zimmer' WHEN 'kontakt_org' THEN 'Abteilung' END AS typ, + CASE "True" IN labels(m) WHEN true THEN 'Ja' WHEN false THEN 'Nein' END AS infiziert diff --git a/spitalhygiene/sql/quality/LA_ISH_NBEW_DQ.sql b/spitalhygiene/sql/quality/LA_ISH_NBEW_DQ.sql new file mode 100644 index 0000000..545b888 --- /dev/null +++ b/spitalhygiene/sql/quality/LA_ISH_NBEW_DQ.sql @@ -0,0 +1,38 @@ +SELECT + sum (case when [FALNR] is null then 0 else 1 end) as "FALNR_NONNULLCOUNT", + sum (case when [FALNR] is null then 1 else 0 end) as "FALNR_NULLCOUNT", + sum (case when [LFDNR] is null then 0 else 1 end) as "LFDNR_NONNULLCOUNT", + sum (case when [LFDNR] is null then 1 else 0 end) as "LFDNR_NULLCOUNT", + sum (case when [BEWTY] is null then 0 else 1 end) as "BEWTY_NONNULLCOUNT", + sum (case when [BEWTY] is null then 1 else 0 end) as "BEWTY_NULLCOUNT", + sum (case when [BWART] is null then 0 else 1 end) as "BWART_NONNULLCOUNT", + sum (case when [BWART] is null then 1 else 0 end) as "BWART_NULLCOUNT", + sum (case when [BWIDT] is null then 0 else 1 end) as "BWIDT_NONNULLCOUNT", + sum (case when [BWIDT] is null then 1 else 0 end) as "BWIDT_NULLCOUNT", + sum (case when [BWIZT] is null then 0 else 1 end) as "BWIZT_NONNULLCOUNT", + sum (case when [BWIZT] is null then 1 else 0 end) as "BWIZT_NULLCOUNT", + sum (case when [STATU] is null then 0 else 1 end) as "STATU_NONNULLCOUNT", + sum (case when [STATU] is null then 1 else 0 end) as "STATU_NULLCOUNT", + sum (case when [BWEDT] is null then 0 else 1 end) as "BWEDT_NONNULLCOUNT", + sum (case when [BWEDT] is null then 1 else 0 end) as "BWEDT_NULLCOUNT", + sum (case when [BWEZT] is null then 0 else 1 end) as "BWEZT_NONNULLCOUNT", + sum (case when [BWEZT] is null then 1 else 0 end) as "BWEZT_NULLCOUNT", + sum (case when [LFDREF] is null then 0 else 1 end) as "LFDREF_NONNULLCOUNT", + sum (case when [LFDREF] is null then 1 else 0 end) as "LFDREF_NULLCOUNT", + sum (case when [KZTXT] is null then 0 else 1 end) as "KZTXT_NONNULLCOUNT", + sum (case when [KZTXT] is null then 1 else 0 end) as "KZTXT_NULLCOUNT", + sum (case when [ORGFA] is null then 0 else 1 end) as "ORGFA_NONNULLCOUNT", + sum (case when [ORGFA] is null then 1 else 0 end) as "ORGFA_NULLCOUNT", + sum (case when [ORGPF] is null then 0 else 1 end) as "ORGPF_NONNULLCOUNT", + sum (case when [ORGPF] is null then 1 else 0 end) as "ORGPF_NULLCOUNT", + sum (case when [ORGAU] is null then 0 else 1 end) as "ORGAU_NONNULLCOUNT", + sum (case when [ORGAU] is null then 1 else 0 end) as "ORGAU_NULLCOUNT", + sum (case when [ZIMMR] is null then 0 else 1 end) as "ZIMMR_NONNULLCOUNT", + sum (case when [ZIMMR] is null then 1 else 0 end) as "ZIMMR_NULLCOUNT", + sum (case when [BETT] is null then 0 else 1 end) as "BETT_NONNULLCOUNT", + sum (case when [BETT] is null then 1 else 0 end) as "BETT_NULLCOUNT", + sum (case when [STORN] is null then 0 else 1 end) as "STORN_NONNULLCOUNT", + sum (case when [STORN] is null then 1 else 0 end) as "STORN_NULLCOUNT", + sum (case when [EXTKH] is null then 0 else 1 end) as "EXTKH_NONNULLCOUNT", + sum (case when [EXTKH] is null then 1 else 0 end) as "EXTKH_NULLCOUNT" +FROM [Atelier_DataScience].[atl].[LA_ISH_NBEW] \ No newline at end of file diff --git a/spitalhygiene/sql/quality/LA_ISH_NDIA_NORM_DQ.sql b/spitalhygiene/sql/quality/LA_ISH_NDIA_NORM_DQ.sql new file mode 100644 index 0000000..10fb22e --- /dev/null +++ b/spitalhygiene/sql/quality/LA_ISH_NDIA_NORM_DQ.sql @@ -0,0 +1,13 @@ +SELECT + sum (case when [FALNR] is null then 0 else 1 end) as "FALNR_NONNULLCOUNT", + sum (case when [FALNR] is null then 1 else 0 end) as "FALNR_NULLCOUNT", + sum (case when [DKEY1] is null then 0 else 1 end) as "DKEY1_NONNULLCOUNT", + sum (case when [DKEY1] is null then 1 else 0 end) as "DKEY1_NULLCOUNT", + sum (case when [DKAT1] is null then 0 else 1 end) as "DKAT1_NONNULLCOUNT", + sum (case when [DKAT1] is null then 1 else 0 end) as "DKAT1_NULLCOUNT", + sum (case when [DIADT] is null then 0 else 1 end) as "DIADT_NONNULLCOUNT", + sum (case when [DIADT] is null then 1 else 0 end) as "DIADT_NULLCOUNT", + sum (case when [DRG_CATEGORY] is null then 0 else 1 end) as "DRG_CATEGORY_NONNULLCOUNT", + sum (case when [DRG_CATEGORY] is null then 1 else 0 end) as "DRG_CATEGORY_NULLCOUNT" +FROM [Atelier_DataScience].[atl].[LA_ISH_NDIA_NORM] +WHERE DKEY1 is not NULL and DRG_CATEGORY is not null diff --git a/spitalhygiene/sql/quality/LA_ISH_NDRG_DQ.sql b/spitalhygiene/sql/quality/LA_ISH_NDRG_DQ.sql new file mode 100644 index 0000000..14d20e0 --- /dev/null +++ b/spitalhygiene/sql/quality/LA_ISH_NDRG_DQ.sql @@ -0,0 +1,6 @@ +SELECT + sum (case when [PATCASEID] is null then 0 else 1 end) as "PATCASEID_NONNULLCOUNT", + sum (case when [PATCASEID] is null then 1 else 0 end) as "PATCASEID_NULLCOUNT", + sum (case when [COST_WEIGHT] is null then 0 else 1 end) as "COST_WEIGHT_NONNULLCOUNT", + sum (case when [COST_WEIGHT] is null then 1 else 0 end) as "COST_WEIGHT_NULLCOUNT" +FROM [Atelier_DataScience].[atl].[LA_ISH_NDRG] diff --git a/spitalhygiene/sql/quality/LA_ISH_NFPZ_DQ.sql b/spitalhygiene/sql/quality/LA_ISH_NFPZ_DQ.sql new file mode 100644 index 0000000..222b996 --- /dev/null +++ b/spitalhygiene/sql/quality/LA_ISH_NFPZ_DQ.sql @@ -0,0 +1,14 @@ +SELECT + sum (case when [EARZT] is null then 0 else 1 end) as "EARZT_NONNULLCOUNT", + sum (case when [EARZT] is null then 1 else 0 end) as "EARZT_NULLCOUNT", + sum (case when [FARZT] is null then 0 else 1 end) as "FARZT_NONNULLCOUNT", + sum (case when [FARZT] is null then 1 else 0 end) as "FARZT_NULLCOUNT", + sum (case when [FALNR] is null then 0 else 1 end) as "FALNR_NONNULLCOUNT", + sum (case when [FALNR] is null then 1 else 0 end) as "FALNR_NULLCOUNT", + sum (case when [LFDNR] is null then 0 else 1 end) as "LFDNR_NONNULLCOUNT", + sum (case when [LFDNR] is null then 1 else 0 end) as "LFDNR_NULLCOUNT", + sum (case when [PERNR] is null then 0 else 1 end) as "PERNR_NONNULLCOUNT", + sum (case when [PERNR] is null then 1 else 0 end) as "PERNR_NULLCOUNT", + sum (case when [STORN] is null then 0 else 1 end) as "STORN_NONNULLCOUNT", + sum (case when [STORN] is null then 1 else 0 end) as "STORN_NULLCOUNT" +FROM [Atelier_DataScience].[atl].[LA_ISH_NFPZ] \ No newline at end of file diff --git a/spitalhygiene/sql/quality/LA_ISH_NGPA_DQ.sql b/spitalhygiene/sql/quality/LA_ISH_NGPA_DQ.sql new file mode 100644 index 0000000..1b59adb --- /dev/null +++ b/spitalhygiene/sql/quality/LA_ISH_NGPA_DQ.sql @@ -0,0 +1,22 @@ +SELECT + sum (case when [GPART] is null then 0 else 1 end) as "GPART_NONNULLCOUNT", + sum (case when [GPART] is null then 1 else 0 end) as "GPART_NULLCOUNT", + sum (case when [NAME1] is null then 0 else 1 end) as "NAME1_NONNULLCOUNT", + sum (case when [NAME1] is null then 1 else 0 end) as "NAME1_NULLCOUNT", + sum (case when [NAME2] is null then 0 else 1 end) as "NAME2_NONNULLCOUNT", + sum (case when [NAME2] is null then 1 else 0 end) as "NAME2_NULLCOUNT", + sum (case when [NAME3] is null then 0 else 1 end) as "NAME3_NONNULLCOUNT", + sum (case when [NAME3] is null then 1 else 0 end) as "NAME3_NULLCOUNT", + sum (case when [LAND] is null then 0 else 1 end) as "LAND_NONNULLCOUNT", + sum (case when [LAND] is null then 1 else 0 end) as "LAND_NULLCOUNT", + sum (case when [PSTLZ] is null then 0 else 1 end) as "PSTLZ_NONNULLCOUNT", + sum (case when [PSTLZ] is null then 1 else 0 end) as "PSTLZ_NULLCOUNT", + sum (case when [ORT] is null then 0 else 1 end) as "ORT_NONNULLCOUNT", + sum (case when [ORT] is null then 1 else 0 end) as "ORT_NULLCOUNT", + sum (case when [ORT2] is null then 0 else 1 end) as "ORT2_NONNULLCOUNT", + sum (case when [ORT2] is null then 1 else 0 end) as "ORT2_NULLCOUNT", + sum (case when [STRAS] is null then 0 else 1 end) as "STRAS_NONNULLCOUNT", + sum (case when [STRAS] is null then 1 else 0 end) as "STRAS_NULLCOUNT", + sum (case when [KRKHS] is null then 0 else 1 end) as "KRKHS_NONNULLCOUNT", + sum (case when [KRKHS] is null then 1 else 0 end) as "KRKHS_NULLCOUNT" +FROM [Atelier_DataScience].[atl].[LA_ISH_NGPA] \ No newline at end of file diff --git a/spitalhygiene/sql/quality/LA_ISH_NICP_DQ.sql b/spitalhygiene/sql/quality/LA_ISH_NICP_DQ.sql new file mode 100644 index 0000000..5c49dd8 --- /dev/null +++ b/spitalhygiene/sql/quality/LA_ISH_NICP_DQ.sql @@ -0,0 +1,20 @@ +SELECT + sum (case when [LFDBEW] is null then 0 else 1 end) as "LFDBEW_NONNULLCOUNT", + sum (case when [LFDBEW] is null then 1 else 0 end) as "LFDBEW_NULLCOUNT", + sum (case when [ICPMK] is null then 0 else 1 end) as "ICPMK_NONNULLCOUNT", + sum (case when [ICPMK] is null then 1 else 0 end) as "ICPMK_NULLCOUNT", + sum (case when [ICPML] is null then 0 else 1 end) as "ICPML_NONNULLCOUNT", + sum (case when [ICPML] is null then 1 else 0 end) as "ICPML_NULLCOUNT", + sum (case when [ANZOP] is null then 0 else 1 end) as "ANZOP_NONNULLCOUNT", + sum (case when [ANZOP] is null then 1 else 0 end) as "ANZOP_NULLCOUNT", + sum (case when [BGDOP] is null then 0 else 1 end) as "BGDOP_NONNULLCOUNT", + sum (case when [BGDOP] is null then 1 else 0 end) as "BGDOP_NULLCOUNT", + sum (case when [LSLOK] is null then 0 else 1 end) as "LSLOK_NONNULLCOUNT", + sum (case when [LSLOK] is null then 1 else 0 end) as "LSLOK_NULLCOUNT", + sum (case when [STORN] is null then 0 else 1 end) as "STORN_NONNULLCOUNT", + sum (case when [STORN] is null then 1 else 0 end) as "STORN_NULLCOUNT", + sum (case when [FALNR] is null then 0 else 1 end) as "FALNR_NONNULLCOUNT", + sum (case when [FALNR] is null then 1 else 0 end) as "FALNR_NULLCOUNT", + sum (case when [ORGPF] is null then 0 else 1 end) as "ORGPF_NONNULLCOUNT", + sum (case when [ORGPF] is null then 1 else 0 end) as "ORGPF_NULLCOUNT" +FROM [Atelier_DataScience].[atl].[LA_ISH_NICP] \ No newline at end of file diff --git a/spitalhygiene/sql/quality/TACS_DATEN_DQ.sql b/spitalhygiene/sql/quality/TACS_DATEN_DQ.sql new file mode 100644 index 0000000..ce74fbf --- /dev/null +++ b/spitalhygiene/sql/quality/TACS_DATEN_DQ.sql @@ -0,0 +1,26 @@ +SELECT + sum (case when [patient_patientid] is null then 0 else 1 end) as "patient_patientid_NONNULLCOUNT", + sum (case when [patient_patientid] is null then 1 else 0 end) as "patient_patientid_NULLCOUNT", + sum (case when [patient_typ] is null then 0 else 1 end) as "patient_typ_NONNULLCOUNT", + sum (case when [patient_typ] is null then 1 else 0 end) as "patient_typ_NULLCOUNT", + sum (case when [patient_status] is null then 0 else 1 end) as "patient_status_NONNULLCOUNT", + sum (case when [patient_status] is null then 1 else 0 end) as "patient_status_NULLCOUNT", + sum (case when [fall_nummer] is null then 0 else 1 end) as "fall_nummer_NONNULLCOUNT", + sum (case when [fall_nummer] is null then 1 else 0 end) as "fall_nummer_NULLCOUNT", + sum (case when [fall_typ] is null then 0 else 1 end) as "fall_typ_NONNULLCOUNT", + sum (case when [fall_typ] is null then 1 else 0 end) as "fall_typ_NULLCOUNT", + sum (case when [fall_status] is null then 0 else 1 end) as "fall_status_NONNULLCOUNT", + sum (case when [fall_status] is null then 1 else 0 end) as "fall_status_NULLCOUNT", + sum (case when [datum_betreuung] is null then 0 else 1 end) as "datum_betreuung_NONNULLCOUNT", + sum (case when [datum_betreuung] is null then 1 else 0 end) as "datum_betreuung_NULLCOUNT", + sum (case when [dauer_betreuung_in_min] is null then 0 else 1 end) as "dauer_betreuung_in_min_NONNULLCOUNT", + sum (case when [dauer_betreuung_in_min] is null then 1 else 0 end) as "dauer_betreuung_in_min_NULLCOUNT", + sum (case when [mitarbeiter_personalnummer] is null then 0 else 1 end) as "mitarbeiter_personalnummer_NONNULLCOUNT", + sum (case when [mitarbeiter_personalnummer] is null then 1 else 0 end) as "mitarbeiter_personalnummer_NULLCOUNT", + sum (case when [mitarbeiter_anstellungsnummer] is null then 0 else 1 end) as "mitarbeiter_anstellungsnummer_NONNULLCOUNT", + sum (case when [mitarbeiter_anstellungsnummer] is null then 1 else 0 end) as "mitarbeiter_anstellungsnummer_NULLCOUNT", + sum (case when [mitarbeiter_login] is null then 0 else 1 end) as "mitarbeiter_login_NONNULLCOUNT", + sum (case when [mitarbeiter_login] is null then 1 else 0 end) as "mitarbeiter_login_NULLCOUNT", + sum (case when [BATCH_RUN_ID] is null then 0 else 1 end) as "BATCH_RUN_ID_NONNULLCOUNT", + sum (case when [BATCH_RUN_ID] is null then 1 else 0 end) as "BATCH_RUN_ID_NULLCOUNT" +FROM [Atelier_DataScience].[atl].[TACS_DATEN] \ No newline at end of file diff --git a/spitalhygiene/sql/quality/V_LA_IPD_DRUG_NORM_DQ.sql b/spitalhygiene/sql/quality/V_LA_IPD_DRUG_NORM_DQ.sql new file mode 100644 index 0000000..06ba5b6 --- /dev/null +++ b/spitalhygiene/sql/quality/V_LA_IPD_DRUG_NORM_DQ.sql @@ -0,0 +1,18 @@ +SELECT + sum (case when [PATIENTID] is null then 0 else 1 end) as "PATIENTID_NONNULLCOUNT", + sum (case when [PATIENTID] is null then 1 else 0 end) as "PATIENTID_NULLCOUNT", + sum (case when [CASEID] is null then 0 else 1 end) as "CASEID_NONNULLCOUNT", + sum (case when [CASEID] is null then 1 else 0 end) as "CASEID_NULLCOUNT", + sum (case when [DRUG_TEXT] is null then 0 else 1 end) as "DRUG_TEXT_NONNULLCOUNT", + sum (case when [DRUG_TEXT] is null then 1 else 0 end) as "DRUG_TEXT_NULLCOUNT", + sum (case when [DRUG_ATC] is null then 0 else 1 end) as "DRUG_ATC_NONNULLCOUNT", + sum (case when [DRUG_ATC] is null then 1 else 0 end) as "DRUG_ATC_NULLCOUNT", + sum (case when [DRUG_QUANTITY] is null then 0 else 1 end) as "DRUG_QUANTITY_NONNULLCOUNT", + sum (case when [DRUG_QUANTITY] is null then 1 else 0 end) as "DRUG_QUANTITY_NULLCOUNT", + sum (case when [DRUG_UNIT] is null then 0 else 1 end) as "DRUG_UNIT_NONNULLCOUNT", + sum (case when [DRUG_UNIT] is null then 1 else 0 end) as "DRUG_UNIT_NULLCOUNT", + sum (case when [DRUG_DISPFORM] is null then 0 else 1 end) as "DRUG_DISPFORM_NONNULLCOUNT", + sum (case when [DRUG_DISPFORM] is null then 1 else 0 end) as "DRUG_DISPFORM_NULLCOUNT", + sum (case when [DRUG_SUBMISSION] is null then 0 else 1 end) as "DRUG_SUBMISSION_NONNULLCOUNT", + sum (case when [DRUG_SUBMISSION] is null then 1 else 0 end) as "DRUG_SUBMISSION_NULLCOUNT" +FROM [Atelier_DataScience].[atl].[V_LA_IPD_DRUG_NORM] \ No newline at end of file diff --git a/spitalhygiene/sql/quality/V_LA_ISH_NFAL_NORM_DQ.sql b/spitalhygiene/sql/quality/V_LA_ISH_NFAL_NORM_DQ.sql new file mode 100644 index 0000000..d9c7b7c --- /dev/null +++ b/spitalhygiene/sql/quality/V_LA_ISH_NFAL_NORM_DQ.sql @@ -0,0 +1,20 @@ +SELECT + sum (case when [PATIENTID] is null then 0 else 1 end) as "PATIENTID_NONNULLCOUNT", + sum (case when [PATIENTID] is null then 1 else 0 end) as "PATIENTID_NULLCOUNT", + sum (case when [CASEID] is null then 0 else 1 end) as "CASEID_NONNULLCOUNT", + sum (case when [CASEID] is null then 1 else 0 end) as "CASEID_NULLCOUNT", + sum (case when [CASETYP] is null then 0 else 1 end) as "CASETYP_NONNULLCOUNT", + sum (case when [CASETYP] is null then 1 else 0 end) as "CASETYP_NULLCOUNT", + sum (case when [CASESTATUS] is null then 0 else 1 end) as "CASESTATUS_NONNULLCOUNT", + sum (case when [CASESTATUS] is null then 1 else 0 end) as "CASESTATUS_NULLCOUNT", + sum (case when [FALAR] is null then 0 else 1 end) as "FALAR_NONNULLCOUNT", + sum (case when [FALAR] is null then 1 else 0 end) as "FALAR_NULLCOUNT", + sum (case when [BEGDT] is null then 0 else 1 end) as "BEGDT_NONNULLCOUNT", + sum (case when [BEGDT] is null then 1 else 0 end) as "BEGDT_NULLCOUNT", + sum (case when [ENDDT] is null then 0 else 1 end) as "ENDDT_NONNULLCOUNT", + sum (case when [ENDDT] is null then 1 else 0 end) as "ENDDT_NULLCOUNT", + sum (case when [PATIENTTYP] is null then 0 else 1 end) as "PATIENTTYP_NONNULLCOUNT", + sum (case when [PATIENTTYP] is null then 1 else 0 end) as "PATIENTTYP_NULLCOUNT", + sum (case when [PATIENTSTATUS] is null then 0 else 1 end) as "PATIENTSTATUS_NONNULLCOUNT", + sum (case when [PATIENTSTATUS] is null then 1 else 0 end) as "PATIENTSTATUS_NULLCOUNT" +FROM [Atelier_DataScience].[atl].[V_LA_ISH_NFAL_NORM] \ No newline at end of file diff --git a/spitalhygiene/sql/quality/V_LA_ISH_NRSF_NORM_DQ.sql b/spitalhygiene/sql/quality/V_LA_ISH_NRSF_NORM_DQ.sql new file mode 100644 index 0000000..3eecc73 --- /dev/null +++ b/spitalhygiene/sql/quality/V_LA_ISH_NRSF_NORM_DQ.sql @@ -0,0 +1,13 @@ +SELECT + sum (case when [PATIENTID] is null then 0 else 1 end) as "PATIENTID_NONNULLCOUNT", + sum (case when [PATIENTID] is null then 1 else 0 end) as "PATIENTID_NULLCOUNT", + sum (case when [RSFNR] is null then 0 else 1 end) as "RSFNR_NONNULLCOUNT", + sum (case when [RSFNR] is null then 1 else 0 end) as "RSFNR_NULLCOUNT", + sum (case when [KZTXT] is null then 0 else 1 end) as "KZTXT_NONNULLCOUNT", + sum (case when [KZTXT] is null then 1 else 0 end) as "KZTXT_NULLCOUNT", + sum (case when [ERDAT] is null then 0 else 1 end) as "ERDAT_NONNULLCOUNT", + sum (case when [ERDAT] is null then 1 else 0 end) as "ERDAT_NULLCOUNT", + sum (case when [ERTIM] is null then 0 else 1 end) as "ERTIM_NONNULLCOUNT", + sum (case when [ERTIM] is null then 1 else 0 end) as "ERTIM_NULLCOUNT" +FROM [Atelier_DataScience].[atl].[V_LA_ISH_NRSF_NORM] +WHERE isnull(LOEKZ,'') <> 'X' \ No newline at end of file diff --git a/spitalhygiene/sql/tests/LA_ISH_NBEW.sql b/spitalhygiene/sql/tests/LA_ISH_NBEW.sql new file mode 100644 index 0000000..93080d8 --- /dev/null +++ b/spitalhygiene/sql/tests/LA_ISH_NBEW.sql @@ -0,0 +1,62 @@ +SELECT + [FALNR] + ,[LFDNR] + ,[BEWTY] + ,[BWART] + ,[BWIDT] + ,[BWIZT] + ,[STATU] + ,[BWEDT] + ,[BWEZT] + ,[LFDREF] + ,[KZTXT] + ,[ORGFA] + ,[ORGPF] + ,[ORGAU] + ,[ZIMMR] + ,[BETT] + ,[STORN] + ,[EXTKH] +FROM [Atelier_DataScience].[atl].[LA_ISH_NBEW] +WHERE FALNR IN ( + '0006280483', + '0006314210', + '0006336438', + '0005889802', + '0005873082', + '0006065973', + '0006091736', + '0006148746', + '0006334066', + '0006059391', + '0005976205', + '0006057834', + '0005983693', + '0006520444', + '0006931777', + '0006812114', + '0005965462', + '0006452545', + '0006433446', + '0006466165', + '0001927876', + '0004555507', + '0004728517', + '0001928507', + '0002802610', + '0004204668', + '0004181978', + '0006951942', + '0005880782', + '0002289902', + '0004411153', + '0004411005', + '0006565152', + '0003962974', + '0006594482', + '0006596375', + '0006551728', + '0005864325', + '0005877026', + '0006069476' + ) diff --git a/spitalhygiene/sql/tests/LA_ISH_NDIA_NORM.sql b/spitalhygiene/sql/tests/LA_ISH_NDIA_NORM.sql new file mode 100644 index 0000000..4f63dec --- /dev/null +++ b/spitalhygiene/sql/tests/LA_ISH_NDIA_NORM.sql @@ -0,0 +1,50 @@ +SELECT FALNR + ,DKEY1 + ,DKAT1 + ,DIADT + ,DRG_CATEGORY +FROM [Atelier_DataScience].[atl].[LA_ISH_NDIA_NORM] +WHERE DKEY1 IS NOT NULL + AND DRG_CATEGORY IS NOT NULL + AND FALNR IN ( + '0006280483' + ,'0006314210' + ,'0006336438' + ,'0005889802' + ,'0005873082' + ,'0006065973' + ,'0006091736' + ,'0006148746' + ,'0006334066' + ,'0006059391' + ,'0005976205' + ,'0006057834' + ,'0005983693' + ,'0006520444' + ,'0006931777' + ,'0006812114' + ,'0005965462' + ,'0006452545' + ,'0006433446' + ,'0006466165' + ,'0001927876' + ,'0004555507' + ,'0004728517' + ,'0001928507' + ,'0002802610' + ,'0004204668' + ,'0004181978' + ,'0006951942' + ,'0005880782' + ,'0002289902' + ,'0004411153' + ,'0004411005' + ,'0006565152' + ,'0003962974' + ,'0006594482' + ,'0006596375' + ,'0006551728' + ,'0005864325' + ,'0005877026' + ,'0006069476' + ) diff --git a/spitalhygiene/sql/tests/LA_ISH_NDRG.sql b/spitalhygiene/sql/tests/LA_ISH_NDRG.sql new file mode 100644 index 0000000..410b275 --- /dev/null +++ b/spitalhygiene/sql/tests/LA_ISH_NDRG.sql @@ -0,0 +1,5 @@ +SELECT + [PATCASEID], + [COST_WEIGHT] +FROM [Atelier_DataScience].[atl].[LA_ISH_NDRG] +where PATCASEID in ('0005976205','0004204668','0005873082','0006594482','0006148746','0005877026','0006565152','0005983693','0006452545','0006057834','0005965462','0005889802','0006551728','0006065973','0004411005','0004411153','0006280483','0006466165','0006091736','0006433446','0005880782','0004555507','0006314210','0004181978','0005864325','0006596375','0002802610','0006334066','0002289902','0006069476','0006336438','0006520444','0006059391','0001927876','0001928507','0003962974','0004728517') diff --git a/spitalhygiene/sql/tests/LA_ISH_NFPZ.sql b/spitalhygiene/sql/tests/LA_ISH_NFPZ.sql new file mode 100644 index 0000000..16a6bc1 --- /dev/null +++ b/spitalhygiene/sql/tests/LA_ISH_NFPZ.sql @@ -0,0 +1,50 @@ +SELECT + [EARZT] + ,[FARZT] + ,[FALNR] + ,[LFDNR] + ,[PERNR] + ,[STORN] +FROM [Atelier_DataScience].[atl].[LA_ISH_NFPZ] +WHERE FALNR IN ( + '0006280483', + '0006314210', + '0006336438', + '0005889802', + '0005873082', + '0006065973', + '0006091736', + '0006148746', + '0006334066', + '0006059391', + '0005976205', + '0006057834', + '0005983693', + '0006520444', + '0006931777', + '0006812114', + '0005965462', + '0006452545', + '0006433446', + '0006466165', + '0001927876', + '0004555507', + '0004728517', + '0001928507', + '0002802610', + '0004204668', + '0004181978', + '0006951942', + '0005880782', + '0002289902', + '0004411153', + '0004411005', + '0006565152', + '0003962974', + '0006594482', + '0006596375', + '0006551728', + '0005864325', + '0005877026', + '0006069476' + ) diff --git a/spitalhygiene/sql/tests/LA_ISH_NGPA.sql b/spitalhygiene/sql/tests/LA_ISH_NGPA.sql new file mode 100644 index 0000000..f038cc2 --- /dev/null +++ b/spitalhygiene/sql/tests/LA_ISH_NGPA.sql @@ -0,0 +1,12 @@ +SELECT [GPART] + ,[NAME1] + ,[NAME2] + ,[NAME3] + ,[LAND] + ,[PSTLZ] + ,[ORT] + ,[ORT2] + ,[STRAS] + ,[KRKHS] +FROM [Atelier_DataScience].[atl].[LA_ISH_NGPA] +WHERE GPART IN ('0010000990') diff --git a/spitalhygiene/sql/tests/LA_ISH_NICP.sql b/spitalhygiene/sql/tests/LA_ISH_NICP.sql new file mode 100644 index 0000000..78c4966 --- /dev/null +++ b/spitalhygiene/sql/tests/LA_ISH_NICP.sql @@ -0,0 +1,51 @@ +SELECT [LFDBEW] + ,[ICPMK] + ,[ICPML] + ,[ANZOP] + ,[BGDOP] + ,[LSLOK] + ,[STORN] + ,[FALNR] + ,[ORGPF] +FROM [Atelier_DataScience].[atl].[LA_ISH_NICP] +WHERE FALNR IN ( + '0006314210' + ,'0006336438' + ,'0005889802' + ,'0005873082' + ,'0006065973' + ,'0006091736' + ,'0006148746' + ,'0006334066' + ,'0006059391' + ,'0005976205' + ,'0006057834' + ,'0005983693' + ,'0006520444' + ,'0006931777' + ,'0006812114' + ,'0005965462' + ,'0006452545' + ,'0006433446' + ,'0006466165' + ,'0001927876' + ,'0004555507' + ,'0004728517' + ,'0001928507' + ,'0002802610' + ,'0004204668' + ,'0004181978' + ,'0006951942' + ,'0005880782' + ,'0002289902' + ,'0004411153' + ,'0004411005' + ,'0006565152' + ,'0003962974' + ,'0006594482' + ,'0006596375' + ,'0006551728' + ,'0005864325' + ,'0005877026' + ,'0006069476' + ) diff --git a/spitalhygiene/sql/tests/OBSOLETE_deleted_screenings.sql b/spitalhygiene/sql/tests/OBSOLETE_deleted_screenings.sql new file mode 100644 index 0000000..0fe4c5c --- /dev/null +++ b/spitalhygiene/sql/tests/OBSOLETE_deleted_screenings.sql @@ -0,0 +1,10 @@ +SELECT + NPAT.VNAME + , NPAT.NNAME + , NPAT.PATIENTID + , NPAT.GBDAT + , CASE WHEN len(Screening1Datum)>1 THEN CONVERT(date, Screening1Datum, 104) END AS ScreeningDate + FROM [Atelier_DataScience].[dbo].[deleted_screenings] as D + LEFT OUTER JOIN [Atelier_DataScience].[atl].[V_LA_ISH_NPAT_NORM] as NPAT ON + D.Name = NPAT.NNAME AND D.Vorname = NPAT.VNAME AND CONVERT(date, DatumGeboren, 104) = NPAT.GBDAT + WHERE NPAT.PATIENTID = '00003067149' \ No newline at end of file diff --git a/spitalhygiene/sql/tests/TACS_DATEN.sql b/spitalhygiene/sql/tests/TACS_DATEN.sql new file mode 100644 index 0000000..f82639a --- /dev/null +++ b/spitalhygiene/sql/tests/TACS_DATEN.sql @@ -0,0 +1,14 @@ +SELECT [patient_patientid] + ,[patient_typ] + ,[patient_status] + ,[fall_nummer] + ,[fall_typ] + ,[fall_status] + ,[datum_betreuung] + ,[dauer_betreuung_in_min] + ,[mitarbeiter_personalnummer] + ,[mitarbeiter_anstellungsnummer] + ,[mitarbeiter_login] + ,[BATCH_RUN_ID] +FROM [Atelier_DataScience].[atl].[TACS_DATEN] +WHERE patient_patientid IN ('00003067149', '00008301433', '00004348346') diff --git a/spitalhygiene/sql/tests/V_DH_DIM_GERAET_CUR.sql b/spitalhygiene/sql/tests/V_DH_DIM_GERAET_CUR.sql new file mode 100644 index 0000000..e640db3 --- /dev/null +++ b/spitalhygiene/sql/tests/V_DH_DIM_GERAET_CUR.sql @@ -0,0 +1,35 @@ +SELECT [GERAETID] + ,[GERAETNAME] +FROM [Atelier_DataScience].[atl].[V_DH_DIM_GERAET_CUR] +WHERE GERAETID IN ( + '134074' + ,'125922' + ,'137160' + ,'125916' + ,'125981' + ,'125981' + ,'64174' + ,'125921' + ,'125981' + ,'125981' + ,'125981' + ,'125981' + ,'125981' + ,'28609' + ,'86293' + ,'125981' + ,'125981' + ,'125981' + ,'86293' + ,'125981' + ,'125981' + ,'64174' + ,'125981' + ,'125981' + ,'125981' + ,'125981' + ,'125981' + ,'125974' + ,'28609' + ,'125981' + ) diff --git a/spitalhygiene/sql/tests/V_DH_DIM_PATIENT_CUR.sql b/spitalhygiene/sql/tests/V_DH_DIM_PATIENT_CUR.sql new file mode 100644 index 0000000..866d525 --- /dev/null +++ b/spitalhygiene/sql/tests/V_DH_DIM_PATIENT_CUR.sql @@ -0,0 +1,15 @@ +SELECT + [PATIENTID] + ,[GESCHLECHT] = CASE GSCHL + WHEN 1 THEN 'männlich' + WHEN 2 THEN 'weiblich' + ELSE 'Unbekannt' + END + ,[GEBURTSDATUM] = GBDAT + ,[PLZ] = PSTLZ + ,[WOHNORT] = ORT + ,[KANTON] = BLAND + ,[SPRACHE] = SPRAS_TEXT +FROM [Atelier_DataScience].[atl].[V_LA_ISH_NPAT_NORM] +WHERE PATIENTID IN ('00003067149', '00008301433', '00004348346') + diff --git a/spitalhygiene/sql/tests/V_DH_DIM_RAUM_CUR.sql b/spitalhygiene/sql/tests/V_DH_DIM_RAUM_CUR.sql new file mode 100644 index 0000000..19ada76 --- /dev/null +++ b/spitalhygiene/sql/tests/V_DH_DIM_RAUM_CUR.sql @@ -0,0 +1,38 @@ +SELECT + [RAUMID], + [RAUMNAME] +FROM [Atelier_DataScience].[atl].[V_DH_DIM_RAUM_CUR] +WHERE RAUMID in ( +'140363', +'142476', +'61994', +'134701', +'128842', +'140371', +'127804', +'133962', +'90408', +'82813', +'123978', +'140417', +'127803', +'140370', +'128840', +'123499', +'124367', +'64855', +'140366', +'140360', +'140362', +'140369', +'140367', +'124368', +'90409', +'123981', +'125514', +'90411', +'125417', +'140372', +'140361', +'140368' +) \ No newline at end of file diff --git a/spitalhygiene/sql/tests/V_DH_DIM_TERMIN_CUR.sql b/spitalhygiene/sql/tests/V_DH_DIM_TERMIN_CUR.sql new file mode 100644 index 0000000..a0c52f5 --- /dev/null +++ b/spitalhygiene/sql/tests/V_DH_DIM_TERMIN_CUR.sql @@ -0,0 +1,108 @@ +SELECT [TERMINID] + ,[IS_DELETED] + ,[TERMINBEZEICHNUNG] + ,[TERMINART] + ,[TERMINTYP] + ,[TERMINDATUM] + ,[DAUERINMIN] +FROM [Atelier_DataScience].[atl].[V_DH_DIM_TERMIN_CUR] +WHERE TERMINID IN ( + '38515699' + ,'38321122' + ,'35416924' + ,'1164130 ' + ,'38470639' + ,'41827160' + ,'39893063' + ,'38411180' + ,'35571391' + ,'35130813' + ,'36160483' + ,'40766840' + ,'42155710' + ,'39491988' + ,'36067632' + ,'37374631' + ,'36129549' + ,'39001478' + ,'39425469' + ,'34338471' + ,'35630084' + ,'35139096' + ,'38431954' + ,'38452040' + ,'40344805' + ,'13831398' + ,'38063644' + ,'38539785' + ,'34220024' + ,'39819467' + ,'39423020' + ,'38386995' + ,'42394432' + ,'38446243' + ,'42213628' + ,'38565198' + ,'39893320' + ,'37244357' + ,'37554138' + ,'41124954' + ,'39051017' + ,'36129560' + ,'35621237' + ,'38772701' + ,'21130116' + ,'38063650' + ,'39608858' + ,'39427731' + ,'21131159' + ,'38331618' + ,'38062724' + ,'24171386' + ,'14908956' + ,'41909560' + ,'39114133' + ,'14091256' + ,'38939623' + ,'35626775' + ,'35139491' + ,'36006751' + ,'38329080' + ,'41909690' + ,'35130747' + ,'36129541' + ,'1278803 ' + ,'38507433' + ,'1192059 ' + ,'39456191' + ,'14091249' + ,'39933520' + ,'24291359' + ,'36071093' + ,'36160474' + ,'19096210' + ,'40218521' + ,'1162144 ' + ,'38660148' + ,'42211133' + ,'39613790' + ,'24230235' + ,'38262758' + ,'35417252' + ,'19252406' + ,'39215737' + ,'38446041' + ,'36830543' + ,'35200182' + ,'40766156' + ,'36070942' + ,'34310589' + ,'37232112' + ,'34337667' + ,'38446523' + ,'34482529' + ,'17297480' + ,'39298995' + ,'36830574' + ,'1405150' + ) diff --git a/spitalhygiene/sql/tests/V_DH_FACT_TERMINGERAET.sql b/spitalhygiene/sql/tests/V_DH_FACT_TERMINGERAET.sql new file mode 100644 index 0000000..8b8c0e6 --- /dev/null +++ b/spitalhygiene/sql/tests/V_DH_FACT_TERMINGERAET.sql @@ -0,0 +1,106 @@ +SELECT [TERMINID] + ,[GERAETID] + ,[TERMINSTART_TS] + ,[TERMINENDE_TS] + ,[DAUERINMIN] +FROM [Atelier_DataScience].[atl].[V_DH_FACT_TERMINGERAET] +WHERE TERMINID IN ( + '38515699' + ,'38321122' + ,'35416924' + ,'1164130 ' + ,'38470639' + ,'41827160' + ,'39893063' + ,'38411180' + ,'35571391' + ,'35130813' + ,'36160483' + ,'40766840' + ,'42155710' + ,'39491988' + ,'36067632' + ,'37374631' + ,'36129549' + ,'39001478' + ,'39425469' + ,'34338471' + ,'35630084' + ,'35139096' + ,'38431954' + ,'38452040' + ,'40344805' + ,'13831398' + ,'38063644' + ,'38539785' + ,'34220024' + ,'39819467' + ,'39423020' + ,'38386995' + ,'42394432' + ,'38446243' + ,'42213628' + ,'38565198' + ,'39893320' + ,'37244357' + ,'37554138' + ,'41124954' + ,'39051017' + ,'36129560' + ,'35621237' + ,'38772701' + ,'21130116' + ,'38063650' + ,'39608858' + ,'39427731' + ,'21131159' + ,'38331618' + ,'38062724' + ,'24171386' + ,'14908956' + ,'41909560' + ,'39114133' + ,'14091256' + ,'38939623' + ,'35626775' + ,'35139491' + ,'36006751' + ,'38329080' + ,'41909690' + ,'35130747' + ,'36129541' + ,'1278803 ' + ,'38507433' + ,'1192059 ' + ,'39456191' + ,'14091249' + ,'39933520' + ,'24291359' + ,'36071093' + ,'36160474' + ,'19096210' + ,'40218521' + ,'1162144 ' + ,'38660148' + ,'42211133' + ,'39613790' + ,'24230235' + ,'38262758' + ,'35417252' + ,'19252406' + ,'39215737' + ,'38446041' + ,'36830543' + ,'35200182' + ,'40766156' + ,'36070942' + ,'34310589' + ,'37232112' + ,'34337667' + ,'38446523' + ,'34482529' + ,'17297480' + ,'39298995' + ,'36830574' + ,'1405150' + ) diff --git a/spitalhygiene/sql/tests/V_DH_FACT_TERMINMITARBEITER.sql b/spitalhygiene/sql/tests/V_DH_FACT_TERMINMITARBEITER.sql new file mode 100644 index 0000000..7a00b5d --- /dev/null +++ b/spitalhygiene/sql/tests/V_DH_FACT_TERMINMITARBEITER.sql @@ -0,0 +1,106 @@ +SELECT [TERMINID] + ,[MITARBEITERID] + ,[TERMINSTART_TS] + ,[TERMINENDE_TS] + ,[DAUERINMIN] +FROM [Atelier_DataScience].[atl].[V_DH_FACT_TERMINMITARBEITER] +WHERE TERMINID IN ( + '38515699' + ,'38321122' + ,'35416924' + ,'1164130 ' + ,'38470639' + ,'41827160' + ,'39893063' + ,'38411180' + ,'35571391' + ,'35130813' + ,'36160483' + ,'40766840' + ,'42155710' + ,'39491988' + ,'36067632' + ,'37374631' + ,'36129549' + ,'39001478' + ,'39425469' + ,'34338471' + ,'35630084' + ,'35139096' + ,'38431954' + ,'38452040' + ,'40344805' + ,'13831398' + ,'38063644' + ,'38539785' + ,'34220024' + ,'39819467' + ,'39423020' + ,'38386995' + ,'42394432' + ,'38446243' + ,'42213628' + ,'38565198' + ,'39893320' + ,'37244357' + ,'37554138' + ,'41124954' + ,'39051017' + ,'36129560' + ,'35621237' + ,'38772701' + ,'21130116' + ,'38063650' + ,'39608858' + ,'39427731' + ,'21131159' + ,'38331618' + ,'38062724' + ,'24171386' + ,'14908956' + ,'41909560' + ,'39114133' + ,'14091256' + ,'38939623' + ,'35626775' + ,'35139491' + ,'36006751' + ,'38329080' + ,'41909690' + ,'35130747' + ,'36129541' + ,'1278803 ' + ,'38507433' + ,'1192059 ' + ,'39456191' + ,'14091249' + ,'39933520' + ,'24291359' + ,'36071093' + ,'36160474' + ,'19096210' + ,'40218521' + ,'1162144 ' + ,'38660148' + ,'42211133' + ,'39613790' + ,'24230235' + ,'38262758' + ,'35417252' + ,'19252406' + ,'39215737' + ,'38446041' + ,'36830543' + ,'35200182' + ,'40766156' + ,'36070942' + ,'34310589' + ,'37232112' + ,'34337667' + ,'38446523' + ,'34482529' + ,'17297480' + ,'39298995' + ,'36830574' + ,'1405150' + ) diff --git a/spitalhygiene/sql/tests/V_DH_FACT_TERMINPATIENT.sql b/spitalhygiene/sql/tests/V_DH_FACT_TERMINPATIENT.sql new file mode 100644 index 0000000..f9bd843 --- /dev/null +++ b/spitalhygiene/sql/tests/V_DH_FACT_TERMINPATIENT.sql @@ -0,0 +1,5 @@ +SELECT [TERMINID] + ,[PATIENTID] + ,[FALLID] +FROM [Atelier_DataScience].[atl].[V_DH_FACT_TERMINPATIENT] +WHERE PATIENTID in ('00003067149', '00008301433', '00004348346') diff --git a/spitalhygiene/sql/tests/V_DH_FACT_TERMINRAUM.sql b/spitalhygiene/sql/tests/V_DH_FACT_TERMINRAUM.sql new file mode 100644 index 0000000..d2cd010 --- /dev/null +++ b/spitalhygiene/sql/tests/V_DH_FACT_TERMINRAUM.sql @@ -0,0 +1,110 @@ +SELECT [TERMINID] + ,FACT_TERMINRAUM.[RAUMID] + ,DIM_RAUM.RAUMNAME + ,[TERMINSTART_TS] + ,[TERMINENDE_TS] + ,[DAUERINMIN] +FROM [Atelier_DataScience].[atl].[V_DH_FACT_TERMINRAUM] as FACT_TERMINRAUM + LEFT JOIN [Atelier_DataScience].[atl].V_DH_DIM_RAUM_CUR as DIM_RAUM + ON FACT_TERMINRAUM.RAUMID = DIM_RAUM.RAUMID +WHERE DIM_RAUM.RAUMNAME IS NOT NULL + AND TERMINID IN ( + '38515699' + ,'38321122' + ,'35416924' + ,'1164130 ' + ,'38470639' + ,'41827160' + ,'39893063' + ,'38411180' + ,'35571391' + ,'35130813' + ,'36160483' + ,'40766840' + ,'42155710' + ,'39491988' + ,'36067632' + ,'37374631' + ,'36129549' + ,'39001478' + ,'39425469' + ,'34338471' + ,'35630084' + ,'35139096' + ,'38431954' + ,'38452040' + ,'40344805' + ,'13831398' + ,'38063644' + ,'38539785' + ,'34220024' + ,'39819467' + ,'39423020' + ,'38386995' + ,'42394432' + ,'38446243' + ,'42213628' + ,'38565198' + ,'39893320' + ,'37244357' + ,'37554138' + ,'41124954' + ,'39051017' + ,'36129560' + ,'35621237' + ,'38772701' + ,'21130116' + ,'38063650' + ,'39608858' + ,'39427731' + ,'21131159' + ,'38331618' + ,'38062724' + ,'24171386' + ,'14908956' + ,'41909560' + ,'39114133' + ,'14091256' + ,'38939623' + ,'35626775' + ,'35139491' + ,'36006751' + ,'38329080' + ,'41909690' + ,'35130747' + ,'36129541' + ,'1278803 ' + ,'38507433' + ,'1192059 ' + ,'39456191' + ,'14091249' + ,'39933520' + ,'24291359' + ,'36071093' + ,'36160474' + ,'19096210' + ,'40218521' + ,'1162144 ' + ,'38660148' + ,'42211133' + ,'39613790' + ,'24230235' + ,'38262758' + ,'35417252' + ,'19252406' + ,'39215737' + ,'38446041' + ,'36830543' + ,'35200182' + ,'40766156' + ,'36070942' + ,'34310589' + ,'37232112' + ,'34337667' + ,'38446523' + ,'34482529' + ,'17297480' + ,'39298995' + ,'36830574' + ,'1405150' + ) diff --git a/spitalhygiene/sql/tests/V_DH_REF_CHOP.sql b/spitalhygiene/sql/tests/V_DH_REF_CHOP.sql new file mode 100644 index 0000000..95efbc1 --- /dev/null +++ b/spitalhygiene/sql/tests/V_DH_REF_CHOP.sql @@ -0,0 +1,76 @@ +SELECT CHOPCODE + ,CHOPVERWENDUNGSJAHR + ,CHOP + ,CHOPCODELEVEL1 + ,CHOPLEVEL1 + ,CHOPCODELEVEL2 + ,CHOPLEVEL2 + ,CHOPCODELEVEL3 + ,CHOPLEVEL3 + ,CHOPCODELEVEL4 + ,CHOPLEVEL4 + ,CHOPCODELEVEL5 + ,CHOPLEVEL5 + ,CHOPCODELEVEL6 + ,CHOPLEVEL6 + ,CHOPSTATUS + ,CHOPSAPKATALOGID +FROM [Atelier_DataScience].[atl].[V_DH_REF_CHOP] +WHERE CHOPCODE in ( + 'Z99.B8.11' + ,'Z50.27.32' + ,'Z00.99.60' + ,'Z88.38.60' + ,'Z99.85' + ,'Z89.07.24' + ,'Z50.27.32' + ,'Z00.99.60' + ,'Z88.38.60' + ,'Z99.85' + ,'Z50.23.13' + ,'Z50.12.09' + ,'Z88.79.50' + ,'Z00.9A.13' + ,'Z39.32.41' + ,'Z50.93' + ,'Z34.84' + ,'Z34.89.99' + ,'Z39.29.89' + ,'Z50.52' + ,'Z00.93.99' + ,'Z00.90.99' + ,'Z00.99.10' + ,'Z50.27.32' + ,'Z00.99.60' + ,'Z88.38.60' + ,'Z99.85' + ,'Z99.04.10' + ,'Z94.8X.40' + ,'Z99.B7.12' + ,'Z99.07.3C' + ,'Z99.04.15' + ,'Z99.05.47' + ,'Z99.B7.13' + ,'Z99.0A' + ,'Z99.28.11' + ,'Z50.52' + ,'Z54.52' + ,'Z00.93.99' + ,'Z00.90.99' + ,'Z51.22.11' + ,'Z39.29.89' + ,'Z99.00' + ,'Z54.12.11' + ,'Z50.12.12' + ,'Z88.79.50' + ,'Z54.25' + ,'Z36.11.22' + ,'Z36.11.26' + ,'Z36.1C.12' + ,'Z39.61.10' + ,'Z39.63' + ,'Z39.64' + ,'Z88.79.50' + ,'Z01.16.12' + ,'Z99.00' + ) diff --git a/spitalhygiene/sql/tests/V_LA_IPD_DRUG_NORM.sql b/spitalhygiene/sql/tests/V_LA_IPD_DRUG_NORM.sql new file mode 100644 index 0000000..a31cd16 --- /dev/null +++ b/spitalhygiene/sql/tests/V_LA_IPD_DRUG_NORM.sql @@ -0,0 +1,11 @@ +SELECT + [PATIENTID] + ,[CASEID] + ,[DRUG_TEXT] + ,[DRUG_ATC] + ,[DRUG_QUANTITY] + ,[DRUG_UNIT] + ,[DRUG_DISPFORM] + ,[DRUG_SUBMISSION] +FROM [Atelier_DataScience].[atl].[V_LA_IPD_DRUG_NORM] +WHERE PATIENTID IN ('00003067149', '00008301433', '00004348346') diff --git a/spitalhygiene/sql/tests/V_LA_ISH_NFAL_NORM.sql b/spitalhygiene/sql/tests/V_LA_ISH_NFAL_NORM.sql new file mode 100644 index 0000000..78049bf --- /dev/null +++ b/spitalhygiene/sql/tests/V_LA_ISH_NFAL_NORM.sql @@ -0,0 +1,12 @@ +SELECT + [PATIENTID] + ,[CASEID] + ,[CASETYP] + ,[CASESTATUS] + ,[FALAR] + ,[BEGDT] + ,[ENDDT] + ,[PATIENTTYP] + ,[PATIENTSTATUS] +FROM [Atelier_DataScience].[atl].[V_LA_ISH_NFAL_NORM] +WHERE PATIENTID IN ('00003067149', '00008301433', '00004348346') diff --git a/spitalhygiene/sql/tests/V_LA_ISH_NRSF_NORM.sql b/spitalhygiene/sql/tests/V_LA_ISH_NRSF_NORM.sql new file mode 100644 index 0000000..8485b6c --- /dev/null +++ b/spitalhygiene/sql/tests/V_LA_ISH_NRSF_NORM.sql @@ -0,0 +1,8 @@ +SELECT + [PATIENTID], + [RSFNR], + [KZTXT], + [ERDAT], + [ERTIM] +FROM [Atelier_DataScience].[atl].[V_LA_ISH_NRSF_NORM] +WHERE PATIENTID in ('00003067149', '00008301433', '00004348346') \ No newline at end of file diff --git a/spitalhygiene/sql/vre/LA_ISH_NBEW.sql b/spitalhygiene/sql/vre/LA_ISH_NBEW.sql new file mode 100644 index 0000000..fb3da0a --- /dev/null +++ b/spitalhygiene/sql/vre/LA_ISH_NBEW.sql @@ -0,0 +1,20 @@ +SELECT + [FALNR] + ,[LFDNR] + ,[BEWTY] + ,[BWART] + ,[BWIDT] + ,[BWIZT] + ,[STATU] + ,[BWEDT] + ,[BWEZT] + ,[LFDREF] + ,[KZTXT] + ,[ORGFA] + ,[ORGPF] + ,[ORGAU] + ,[ZIMMR] + ,[BETT] + ,[STORN] + ,[EXTKH] +FROM [Atelier_DataScience].[atl].[LA_ISH_NBEW] diff --git a/spitalhygiene/sql/vre/LA_ISH_NDIA_NORM.sql b/spitalhygiene/sql/vre/LA_ISH_NDIA_NORM.sql new file mode 100644 index 0000000..0a20946 --- /dev/null +++ b/spitalhygiene/sql/vre/LA_ISH_NDIA_NORM.sql @@ -0,0 +1,8 @@ +SELECT FALNR + ,DKEY1 + ,DKAT1 + ,DIADT + ,DRG_CATEGORY +FROM [Atelier_DataScience].[atl].[LA_ISH_NDIA_NORM] +WHERE DKEY1 IS NOT NULL + AND DRG_CATEGORY IS NOT NULL diff --git a/spitalhygiene/sql/vre/LA_ISH_NDRG.sql b/spitalhygiene/sql/vre/LA_ISH_NDRG.sql new file mode 100644 index 0000000..3af61d4 --- /dev/null +++ b/spitalhygiene/sql/vre/LA_ISH_NDRG.sql @@ -0,0 +1,4 @@ +SELECT + [PATCASEID], + [COST_WEIGHT] +FROM [Atelier_DataScience].[atl].[LA_ISH_NDRG] diff --git a/spitalhygiene/sql/vre/LA_ISH_NFPZ.sql b/spitalhygiene/sql/vre/LA_ISH_NFPZ.sql new file mode 100644 index 0000000..1db4e2e --- /dev/null +++ b/spitalhygiene/sql/vre/LA_ISH_NFPZ.sql @@ -0,0 +1,8 @@ +SELECT + [EARZT] + ,[FARZT] + ,[FALNR] + ,[LFDNR] + ,[PERNR] + ,[STORN] +FROM [Atelier_DataScience].[atl].[LA_ISH_NFPZ] diff --git a/spitalhygiene/sql/vre/LA_ISH_NGPA.sql b/spitalhygiene/sql/vre/LA_ISH_NGPA.sql new file mode 100644 index 0000000..d79878e --- /dev/null +++ b/spitalhygiene/sql/vre/LA_ISH_NGPA.sql @@ -0,0 +1,11 @@ +SELECT [GPART] + ,[NAME1] + ,[NAME2] + ,[NAME3] + ,[LAND] + ,[PSTLZ] + ,[ORT] + ,[ORT2] + ,[STRAS] + ,[KRKHS] +FROM [Atelier_DataScience].[atl].[LA_ISH_NGPA] diff --git a/spitalhygiene/sql/vre/LA_ISH_NICP.sql b/spitalhygiene/sql/vre/LA_ISH_NICP.sql new file mode 100644 index 0000000..c872b86 --- /dev/null +++ b/spitalhygiene/sql/vre/LA_ISH_NICP.sql @@ -0,0 +1,10 @@ +SELECT [LFDBEW] + ,[ICPMK] + ,[ICPML] + ,[ANZOP] + ,[BGDOP] + ,[LSLOK] + ,[STORN] + ,[FALNR] + ,[ORGPF] +FROM [Atelier_DataScience].[atl].[LA_ISH_NICP] diff --git a/spitalhygiene/sql/vre/TACS_DATEN.sql b/spitalhygiene/sql/vre/TACS_DATEN.sql new file mode 100644 index 0000000..e9a8242 --- /dev/null +++ b/spitalhygiene/sql/vre/TACS_DATEN.sql @@ -0,0 +1,13 @@ +SELECT [patient_patientid] + ,[patient_typ] + ,[patient_status] + ,[fall_nummer] + ,[fall_typ] + ,[fall_status] + ,[datum_betreuung] + ,[dauer_betreuung_in_min] + ,[mitarbeiter_personalnummer] + ,[mitarbeiter_anstellungsnummer] + ,[mitarbeiter_login] + ,[BATCH_RUN_ID] +FROM [Atelier_DataScience].[atl].[TACS_DATEN] diff --git a/spitalhygiene/sql/vre/V_DH_DIM_GERAET_CUR.sql b/spitalhygiene/sql/vre/V_DH_DIM_GERAET_CUR.sql new file mode 100644 index 0000000..70376dc --- /dev/null +++ b/spitalhygiene/sql/vre/V_DH_DIM_GERAET_CUR.sql @@ -0,0 +1,3 @@ +SELECT [GERAETID] + ,[GERAETNAME] +FROM [Atelier_DataScience].[atl].[V_DH_DIM_GERAET_CUR] diff --git a/spitalhygiene/sql/vre/V_DH_DIM_PATIENT_CUR.sql b/spitalhygiene/sql/vre/V_DH_DIM_PATIENT_CUR.sql new file mode 100644 index 0000000..c3affec --- /dev/null +++ b/spitalhygiene/sql/vre/V_DH_DIM_PATIENT_CUR.sql @@ -0,0 +1,14 @@ +SELECT + [PATIENTID] + ,[GESCHLECHT] = CASE GSCHL + WHEN 1 THEN 'männlich' + WHEN 2 THEN 'weiblich' + ELSE 'Unbekannt' + END + ,[GEBURTSDATUM] = GBDAT + ,[PLZ] = PSTLZ + ,[WOHNORT] = ORT + ,[KANTON] = BLAND + ,[SPRACHE] = SPRAS_TEXT +FROM [Atelier_DataScience].[atl].[V_LA_ISH_NPAT_NORM] + diff --git a/spitalhygiene/sql/vre/V_DH_DIM_RAUM_CUR.sql b/spitalhygiene/sql/vre/V_DH_DIM_RAUM_CUR.sql new file mode 100644 index 0000000..f31d264 --- /dev/null +++ b/spitalhygiene/sql/vre/V_DH_DIM_RAUM_CUR.sql @@ -0,0 +1,4 @@ +SELECT + [RAUMID], + [RAUMNAME] +FROM [Atelier_DataScience].[atl].[V_DH_DIM_RAUM_CUR] \ No newline at end of file diff --git a/spitalhygiene/sql/vre/V_DH_DIM_TERMIN_CUR.sql b/spitalhygiene/sql/vre/V_DH_DIM_TERMIN_CUR.sql new file mode 100644 index 0000000..e050ce2 --- /dev/null +++ b/spitalhygiene/sql/vre/V_DH_DIM_TERMIN_CUR.sql @@ -0,0 +1,8 @@ +SELECT [TERMINID] + ,[IS_DELETED] + ,[TERMINBEZEICHNUNG] + ,[TERMINART] + ,[TERMINTYP] + ,[TERMINDATUM] + ,[DAUERINMIN] +FROM [Atelier_DataScience].[atl].[V_DH_DIM_TERMIN_CUR] diff --git a/spitalhygiene/sql/vre/V_DH_FACT_TERMINGERAET.sql b/spitalhygiene/sql/vre/V_DH_FACT_TERMINGERAET.sql new file mode 100644 index 0000000..71cb106 --- /dev/null +++ b/spitalhygiene/sql/vre/V_DH_FACT_TERMINGERAET.sql @@ -0,0 +1,6 @@ +SELECT [TERMINID] + ,[GERAETID] + ,[TERMINSTART_TS] + ,[TERMINENDE_TS] + ,[DAUERINMIN] +FROM [Atelier_DataScience].[atl].[V_DH_FACT_TERMINGERAET] diff --git a/spitalhygiene/sql/vre/V_DH_FACT_TERMINMITARBEITER.sql b/spitalhygiene/sql/vre/V_DH_FACT_TERMINMITARBEITER.sql new file mode 100644 index 0000000..a89af1f --- /dev/null +++ b/spitalhygiene/sql/vre/V_DH_FACT_TERMINMITARBEITER.sql @@ -0,0 +1,6 @@ +SELECT [TERMINID] + ,[MITARBEITERID] + ,[TERMINSTART_TS] + ,[TERMINENDE_TS] + ,[DAUERINMIN] +FROM [Atelier_DataScience].[atl].[V_DH_FACT_TERMINMITARBEITER] diff --git a/spitalhygiene/sql/vre/V_DH_FACT_TERMINPATIENT.sql b/spitalhygiene/sql/vre/V_DH_FACT_TERMINPATIENT.sql new file mode 100644 index 0000000..5503e61 --- /dev/null +++ b/spitalhygiene/sql/vre/V_DH_FACT_TERMINPATIENT.sql @@ -0,0 +1,4 @@ +SELECT [TERMINID] + ,[PATIENTID] + ,[FALLID] +FROM [Atelier_DataScience].[atl].[V_DH_FACT_TERMINPATIENT] diff --git a/spitalhygiene/sql/vre/V_DH_FACT_TERMINRAUM.sql b/spitalhygiene/sql/vre/V_DH_FACT_TERMINRAUM.sql new file mode 100644 index 0000000..219c0c1 --- /dev/null +++ b/spitalhygiene/sql/vre/V_DH_FACT_TERMINRAUM.sql @@ -0,0 +1,10 @@ +SELECT [TERMINID] + ,FACT_TERMINRAUM.[RAUMID] + ,DIM_RAUM.RAUMNAME + ,[TERMINSTART_TS] + ,[TERMINENDE_TS] + ,[DAUERINMIN] +FROM [Atelier_DataScience].[atl].[V_DH_FACT_TERMINRAUM] as FACT_TERMINRAUM + LEFT JOIN [Atelier_DataScience].[atl].V_DH_DIM_RAUM_CUR as DIM_RAUM + ON FACT_TERMINRAUM.RAUMID = DIM_RAUM.RAUMID +WHERE DIM_RAUM.RAUMNAME IS NOT NULL diff --git a/spitalhygiene/sql/vre/V_DH_REF_CHOP.sql b/spitalhygiene/sql/vre/V_DH_REF_CHOP.sql new file mode 100644 index 0000000..bccd388 --- /dev/null +++ b/spitalhygiene/sql/vre/V_DH_REF_CHOP.sql @@ -0,0 +1,18 @@ +SELECT CHOPCODE + ,CHOPVERWENDUNGSJAHR + ,CHOP + ,CHOPCODELEVEL1 + ,CHOPLEVEL1 + ,CHOPCODELEVEL2 + ,CHOPLEVEL2 + ,CHOPCODELEVEL3 + ,CHOPLEVEL3 + ,CHOPCODELEVEL4 + ,CHOPLEVEL4 + ,CHOPCODELEVEL5 + ,CHOPLEVEL5 + ,CHOPCODELEVEL6 + ,CHOPLEVEL6 + ,CHOPSTATUS + ,CHOPSAPKATALOGID +FROM [Atelier_DataScience].[atl].[V_DH_REF_CHOP] diff --git a/spitalhygiene/sql/vre/V_LA_IPD_DRUG_NORM.sql b/spitalhygiene/sql/vre/V_LA_IPD_DRUG_NORM.sql new file mode 100644 index 0000000..0b0da0a --- /dev/null +++ b/spitalhygiene/sql/vre/V_LA_IPD_DRUG_NORM.sql @@ -0,0 +1,10 @@ +SELECT + [PATIENTID] + ,[CASEID] + ,[DRUG_TEXT] + ,[DRUG_ATC] + ,[DRUG_QUANTITY] + ,[DRUG_UNIT] + ,[DRUG_DISPFORM] + ,[DRUG_SUBMISSION] +FROM [Atelier_DataScience].[atl].[V_LA_IPD_DRUG_NORM] diff --git a/spitalhygiene/sql/vre/V_LA_ISH_NFAL_NORM.sql b/spitalhygiene/sql/vre/V_LA_ISH_NFAL_NORM.sql new file mode 100644 index 0000000..4da7d00 --- /dev/null +++ b/spitalhygiene/sql/vre/V_LA_ISH_NFAL_NORM.sql @@ -0,0 +1,11 @@ +SELECT + [PATIENTID] + ,[CASEID] + ,[CASETYP] + ,[CASESTATUS] + ,[FALAR] + ,[BEGDT] + ,[ENDDT] + ,[PATIENTTYP] + ,[PATIENTSTATUS] +FROM [Atelier_DataScience].[atl].[V_LA_ISH_NFAL_NORM] diff --git a/spitalhygiene/sql/vre/V_LA_ISH_NRSF_NORM.sql b/spitalhygiene/sql/vre/V_LA_ISH_NRSF_NORM.sql new file mode 100644 index 0000000..6a44d0b --- /dev/null +++ b/spitalhygiene/sql/vre/V_LA_ISH_NRSF_NORM.sql @@ -0,0 +1,8 @@ +SELECT + [PATIENTID], + [RSFNR], + [KZTXT], + [ERDAT], + [ERTIM] +FROM [Atelier_DataScience].[atl].[V_LA_ISH_NRSF_NORM] +WHERE isnull(LOEKZ,'') <> 'X' \ No newline at end of file diff --git a/spitalhygiene/sql/vre/deleted_screenings.sql b/spitalhygiene/sql/vre/deleted_screenings.sql new file mode 100644 index 0000000..17fd8b9 --- /dev/null +++ b/spitalhygiene/sql/vre/deleted_screenings.sql @@ -0,0 +1,9 @@ +SELECT + NPAT.VNAME + , NPAT.NNAME + , NPAT.PATIENTID + , NPAT.GBDAT + , CASE WHEN len(Screening1Datum)>1 THEN CONVERT(date, Screening1Datum, 104) END AS ScreeningDate + FROM [Atelier_DataScience].[dbo].[deleted_screenings] as D + LEFT OUTER JOIN [Atelier_DataScience].[atl].[V_LA_ISH_NPAT_NORM] as NPAT ON + D.Name = NPAT.NNAME AND D.Vorname = NPAT.VNAME AND CONVERT(date, DatumGeboren, 104) = NPAT.GBDAT \ No newline at end of file diff --git a/spitalhygiene/vre/pom.xml b/spitalhygiene/vre/pom.xml new file mode 100644 index 0000000..766c9ad --- /dev/null +++ b/spitalhygiene/vre/pom.xml @@ -0,0 +1,32 @@ + + + 4.0.0 + io.sqooba.insel + vre + 0.3.0-SNAPSHOT + + + io.sqooba.insel + spitalhygiene + 0.1.0-SNAPSHOT + + + + + + io.sqooba + maven-python-plugin + 1.1.2 + + + + package + + + + + + + + diff --git a/spitalhygiene/vre/src/main/python/README.md b/spitalhygiene/vre/src/main/python/README.md new file mode 100644 index 0000000..6279494 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/README.md @@ -0,0 +1,17 @@ +# Directory Overview + +Folder `vre` + +Contains all functions relevant for data processing, model creation and feature vector export. + +----- + +Folder `tests`: + +Contains test code for each class in the model. Tests are intended to be run with the `pytest` module as follows: + +- Make sure `test_data_dir` in `BasicConfig.ini` points to a directory containing all test CSV files. This is a small subset of the complete data extract of the Atelier_DataScience +and may **not** be in the repo (i.e. must be made available separately). +- Navigate to the `tests` directory +- Start the tests via `python -m pytest` or `python -m pytest > output.log` + diff --git a/spitalhygiene/vre/src/main/python/setup.py b/spitalhygiene/vre/src/main/python/setup.py new file mode 100644 index 0000000..5899366 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/setup.py @@ -0,0 +1,33 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import os +import codecs +from setuptools import setup, find_packages + + +# Utility function to read the README file. +# Used for the long_description. It's nice, because now 1) we have a top level +# README file and 2) it's easier to type in the README file than to put a raw +# string in below ... +def read(fname): + return open(os.path.join(os.path.dirname(__file__), fname)).read() + + +setup( + name="vre", + description=("Inselspital SpitalHygiene"), + keywords="inselspital vre python", + url="http://bitbucket.org/sqooba/...", # url of source code repository + classifiers=[ + "Topic :: Utilities", + ], + install_requires=[ + "scikit-learn==0.19.1", + "numpy==1.14.5", + "pandas==0.23.0", + "scipy==1.1.0", + "pytest==3.7.1", # for testing + ], + packages=find_packages(), # find all the modules automatically + include_package_data=True, # use MANIFEST.in during install if needed +) \ No newline at end of file diff --git a/spitalhygiene/vre/src/main/python/tests/conftest.py b/spitalhygiene/vre/src/main/python/tests/conftest.py new file mode 100644 index 0000000..0bc13cc --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/conftest.py @@ -0,0 +1,115 @@ +import pytest +import csv +import os +import sys +import configparser +sys.path.append("../vre/model") + +from Patient import Patient +from Case import Case +from Move import Move +from Risk import Risk +from Medication import Medication +from Chop import Chop +from Surgery import Surgery +from Appointment import Appointment +from Device import Device +from Employee import Employee +from Room import Room +from Partner import Partner +from Care import Care +from ICD import ICD + +# Load configuration file +config_reader = configparser.ConfigParser() +config_reader.read('../vre/BasicConfig.ini') + +base_path = config_reader['PATHS']['test_data_dir'] # Must point to the directory containing all CSV testfiles - these are patient data and may NOT be in the repo ! + +patients_path = os.path.join(base_path, "V_DH_DIM_PATIENT_CUR.csv") +cases_path = os.path.join(base_path, "V_LA_ISH_NFAL_NORM.csv") +moves_path = os.path.join(base_path, "LA_ISH_NBEW.csv") +risks_path = os.path.join(base_path, "V_LA_ISH_NRSF_NORM.csv") +deleted_risks_path = os.path.join(base_path, "deleted_screenings.csv") +medication_path = os.path.join(base_path, "V_LA_IPD_DRUG_NORM.csv") +chop_path = os.path.join(base_path, "V_DH_REF_CHOP.csv") +surgery_path = os.path.join(base_path, "LA_ISH_NICP.csv") +appointments_path = os.path.join(base_path, "V_DH_DIM_TERMIN_CUR.csv") +appointment_patient_path = os.path.join(base_path, "V_DH_FACT_TERMINPATIENT.csv") +devices_path = os.path.join(base_path, "V_DH_DIM_GERAET_CUR.csv") +device_appointment_path = os.path.join(base_path, "V_DH_FACT_TERMINGERAET.csv") +appointment_employee_path = os.path.join(base_path, "V_DH_FACT_TERMINMITARBEITER.csv") +rooms_path = os.path.join(base_path, "V_DH_DIM_RAUM_CUR.csv") +room_appointment_path = os.path.join(base_path, "V_DH_FACT_TERMINRAUM.csv") +partner_path = os.path.join(base_path, "LA_ISH_NGPA.csv") +partner_case_path = os.path.join(base_path, "LA_ISH_NFPZ.csv") +tacs_path = os.path.join(base_path, "TACS_DATEN.csv") +icd_path = os.path.join(base_path, "LA_ISH_NDIA_NORM.csv") + +def get_hdfs_pipe(path): + encoding = "iso-8859-1" + lines = csv.reader(open(path, 'r'), delimiter=config_reader['DELIMITERS']['csv_sep']) + next(lines, None) # skip header + return lines + +@pytest.fixture +def patient_data(): + rooms = dict() + wards = dict() + + patients = Patient.create_patient_dict(get_hdfs_pipe(patients_path)) + cases = Case.create_case_map(get_hdfs_pipe(cases_path), patients) + + partners = Partner.create_partner_map(get_hdfs_pipe(partner_path)) + Partner.add_partners_to_cases(get_hdfs_pipe(partner_case_path), cases, partners) + + Move.add_move_to_case( + get_hdfs_pipe(moves_path), + cases, + rooms, + wards, + partners, + ) + Risk.add_risk_to_patient(get_hdfs_pipe(risks_path), patients) + Risk.add_deleted_risk_to_patient(get_hdfs_pipe(deleted_risks_path), patients) + + drugs = Medication.create_drug_map(get_hdfs_pipe(medication_path)) + Medication.add_medication_to_case(get_hdfs_pipe(medication_path), cases) + + chops = Chop.create_chop_dict(get_hdfs_pipe(chop_path)) + Surgery.add_surgery_to_case(get_hdfs_pipe(surgery_path), cases, chops) + + appointments = Appointment.create_termin_map(get_hdfs_pipe(appointments_path)) + Appointment.add_appointment_to_case(get_hdfs_pipe(appointment_patient_path), cases, appointments) + + devices = Device.create_device_map(get_hdfs_pipe(devices_path)) + Device.add_device_to_appointment(get_hdfs_pipe(device_appointment_path), appointments, devices) + + employees = Employee.create_employee_map(get_hdfs_pipe(appointment_employee_path)) + Employee.add_employee_to_appointment( get_hdfs_pipe(appointment_employee_path), appointments, employees ) + + Care.add_care_to_case(get_hdfs_pipe(tacs_path), cases, employees) + + room_id_map = Room.create_room_id_map(get_hdfs_pipe(rooms_path)) + Room.add_room_to_appointment(get_hdfs_pipe(room_appointment_path), appointments, room_id_map, rooms) + + ICD_Codes = ICD.create_icd_dict(get_hdfs_pipe(icd_path)) + ICD.add_icd_to_case(get_hdfs_pipe(icd_path), cases) + + return dict( + { + "rooms": rooms, + "wards": wards, + "partners": partners, + "patients": patients, + "cases": cases, + "drugs": drugs, + "chops": chops, + "appointments": appointments, + "devices": devices, + "employees": employees, + "room_id_map": room_id_map, + "icd_codes": ICD_Codes + } + ) + diff --git a/spitalhygiene/vre/src/main/python/tests/pytest.ini b/spitalhygiene/vre/src/main/python/tests/pytest.ini new file mode 100644 index 0000000..96e7e1a --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/pytest.ini @@ -0,0 +1,7 @@ +[pytest] + +python_files=test_*.py + +log_format = %(asctime)s %(levelname)s %(message)s +log_date_format = %Y-%m-%d %H:%M:%S +log_level = DEBUG diff --git a/spitalhygiene/vre/src/main/python/tests/test_age.py b/spitalhygiene/vre/src/main/python/tests/test_age.py new file mode 100644 index 0000000..bad33da --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_age.py @@ -0,0 +1,3 @@ +def test_age(patient_data): + assert patient_data["patients"].get("00008301433").get_age() == 79 + assert patient_data["patients"].get("00003067149").get_age() == 65 \ No newline at end of file diff --git a/spitalhygiene/vre/src/main/python/tests/test_antibiotic_exposure.py b/spitalhygiene/vre/src/main/python/tests/test_antibiotic_exposure.py new file mode 100644 index 0000000..43e345c --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_antibiotic_exposure.py @@ -0,0 +1,36 @@ +import datetime + + +def test_antibiotic_exposure(patient_data): + assert ( + len(patient_data["patients"].get("00003067149").get_antibiotic_exposure()) == 2 + ) + assert ( + patient_data["patients"] + .get("00008301433") + .get_antibiotic_exposure() + .get("J01DC02") + ) + + +def test_antibiotic_exposure_dates(patient_data): + assert ( + len( + patient_data["patients"] + .get("00008301433") + .get_antibiotic_exposure() + .get("J01DC02") + ) + == 2 + ) + assert datetime.datetime(2018, 7, 7).date() in patient_data["patients"].get( + "00008301433" + ).get_antibiotic_exposure().get("J01DC02") + assert datetime.datetime(2018, 7, 5).date() not in patient_data["patients"].get( + "00008301433" + ).get_antibiotic_exposure().get("J01DC02") + + +def test_dispforms(patient_data): + assert len(patient_data["patients"].get("00008301433").get_dispform()) == 7 + assert "p.o." in patient_data["patients"].get("00008301433").get_dispform() diff --git a/spitalhygiene/vre/src/main/python/tests/test_appointments.py b/spitalhygiene/vre/src/main/python/tests/test_appointments.py new file mode 100644 index 0000000..4367c5f --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_appointments.py @@ -0,0 +1,5 @@ +def test_appointments_load(patient_data): + assert len(patient_data["appointments"]) == 87 + +def test_appointments_to_case(patient_data): + assert len(patient_data["cases"].get("0006452545").appointments) == 7 \ No newline at end of file diff --git a/spitalhygiene/vre/src/main/python/tests/test_care.py b/spitalhygiene/vre/src/main/python/tests/test_care.py new file mode 100644 index 0000000..d36ebf6 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_care.py @@ -0,0 +1,10 @@ +def test_add_care_to_case(patient_data): + sum = 0 + for case in patient_data["cases"].values(): + sum += len(case.cares) + assert sum == 409 + +def test_care_in_employees(patient_data): + for case in patient_data["cases"].values(): + for care in case.cares: + assert care.employee.mitarbeiter_id in list(patient_data["employees"].keys()) diff --git a/spitalhygiene/vre/src/main/python/tests/test_case_timing.py b/spitalhygiene/vre/src/main/python/tests/test_case_timing.py new file mode 100644 index 0000000..deb9b79 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_case_timing.py @@ -0,0 +1,117 @@ +import datetime + + +def test_case_timing(patient_data): + # beg_dt and end_dt are not set in the source data + assert patient_data["cases"].get("0006314210").beg_dt is None + assert patient_data["cases"].get("0006314210").end_dt is None + + # moves_start and moves_end are set to the beginning of the first move and end of the last move respectively + assert patient_data["cases"].get("0006314210").moves_start == datetime.datetime( + 2018, 1, 5, 11, 0 + ) + assert patient_data["cases"].get("0006314210").moves_end == datetime.datetime( + 2018, 1, 16, 13, 0 + ) + + # length of stay is the timedelta between first and last move + assert patient_data["cases"].get( + "0006314210" + ).get_length_of_stay() == datetime.timedelta(11, 7200) + + # length of stay for non-stationary cases must be None + assert patient_data["cases"].get("0006334066").moves_start == datetime.datetime( + 2018, 4, 9, 11, 7 + ) + assert patient_data["cases"].get("0006334066").moves_end == datetime.datetime( + 2018, 8, 17, 10, 56 + ) + assert patient_data["cases"].get("0006334066").get_length_of_stay() is None + + # case 0006594482 is almost 8 days + assert patient_data["cases"].get( + "0006594482" + ).get_length_of_stay() == datetime.timedelta(7, 84600) + + # case 0006594482 is relevant case of patient with risk date during the case. thus, length of relevant stay is shorter + assert patient_data["patients"].get( + "00008301433" + ).get_length_of_relevant_case() == datetime.timedelta(4, 50400) + + +def test_case_stationary(patient_data): + assert patient_data["cases"].get("0005976205").is_stationary() + assert not patient_data["cases"].get("0005965462").is_stationary() + + +def test_case_open_before(patient_data): + # 0005976205 starts at 2017-6-16 and ends at 2017-6-17 + assert ( + patient_data["cases"] + .get("0005976205") + .open_before_or_at_date(datetime.datetime(2017, 6, 16).date()) + ) + assert ( + patient_data["cases"] + .get("0005976205") + .open_before_or_at_date(datetime.datetime(2017, 7, 1).date()) + ) + assert ( + not patient_data["cases"] + .get("0005976205") + .open_before_or_at_date(datetime.datetime(2017, 6, 15).date()) + ) + + +def test_case_closed_after(patient_data): + assert ( + patient_data["cases"] + .get("0005976205") + .closed_after_or_at_date(datetime.datetime(2017, 6, 17).date()) + ) + assert ( + patient_data["cases"] + .get("0005976205") + .closed_after_or_at_date(datetime.datetime(2017, 5, 1).date()) + ) + assert ( + not patient_data["cases"] + .get("0005976205") + .closed_after_or_at_date(datetime.datetime(2017, 7, 1).date()) + ) + + +def test_relevant_date(patient_data): + # for a patient without risk factor, the relevant date is now + assert ( + patient_data["patients"].get("00004348346").get_relevant_date() + == datetime.datetime.now().date() + ) + + # for a patient with risk factor, the relevant date is the date of the risk factor + assert ( + patient_data["patients"].get("00008301433").get_relevant_date() + == datetime.datetime(2018, 7, 9).date() + ) + + +def test_relevant_case(patient_data): + # patient has no stationary case -> no relevant case + assert patient_data["patients"].get("00004348346").get_relevant_case() is None + + # case 0006314210: 5.1.2018 - 16.1.2018 + assert ( + patient_data["patients"].get("00003067149").get_relevant_case().case_id + == "0006314210" + ) + assert ( + patient_data["patients"] + .get("00003067149") + .get_relevant_case(since=datetime.datetime(2018, 2, 1).date()) + is None + ) + + assert ( + patient_data["patients"].get("00008301433").get_relevant_case().case_id + == "0006594482" + ) diff --git a/spitalhygiene/vre/src/main/python/tests/test_chop_codes.py b/spitalhygiene/vre/src/main/python/tests/test_chop_codes.py new file mode 100644 index 0000000..dd0a4a6 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_chop_codes.py @@ -0,0 +1,28 @@ +def test_chop_codes_load(patient_data): + assert len(patient_data["chops"]) == 250 + + +def test_chop_code_description(patient_data): + print(patient_data["chops"].get("Z39.64_18").get_detailed_chop()) + assert ( + patient_data["chops"].get("Z39.64_18").get_detailed_chop() + == "Vorläufiger Schrittmacher eingesetzt während und unmittelbar nach herzchirurgischem Eingriff" # Note weird encoding of german "Umlaut" strings + ) + + +def test_lowest_level_code(patient_data): + assert patient_data["chops"].get("Z39.64_18").get_lowest_level_code() == "Z39" + assert patient_data["chops"].get("Z89.07.24_16").get_lowest_level_code() == "Z89" + + +def test_surgeries_per_chop(patient_data): + assert len(patient_data["chops"].get("Z39.61.10_18").cases) == 1 + + +def test_relevant_chops(patient_data): + # 99.04.10 + assert len(patient_data["patients"].get("00008301433").get_chop_codes()) == 12 + +def test_has_surgery(patient_data): + assert patient_data["patients"].get("00008301433").has_surgery() + assert not patient_data["patients"].get("00004348346").has_surgery() \ No newline at end of file diff --git a/spitalhygiene/vre/src/main/python/tests/test_devices.py b/spitalhygiene/vre/src/main/python/tests/test_devices.py new file mode 100644 index 0000000..f439c60 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_devices.py @@ -0,0 +1,7 @@ +def test_device_load(patient_data): + assert len(patient_data["devices"]) == 10 + + +def test_relevant_devices(patient_data): + assert len(patient_data["patients"].get("00008301433").get_devices()) == 1 + assert "ECC" in patient_data["patients"].get("00008301433").get_devices() diff --git a/spitalhygiene/vre/src/main/python/tests/test_employees.py b/spitalhygiene/vre/src/main/python/tests/test_employees.py new file mode 100644 index 0000000..4bc9057 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_employees.py @@ -0,0 +1,7 @@ +def test_employee_load(patient_data): + assert len(patient_data["employees"]) == 182 + + +def test_relevant_employees(patient_data): + assert len(patient_data["patients"].get("00008301433").get_employees()) == 41 + assert "0030236" in patient_data["patients"].get("00008301433").get_employees() diff --git a/spitalhygiene/vre/src/main/python/tests/test_features.py b/spitalhygiene/vre/src/main/python/tests/test_features.py new file mode 100644 index 0000000..d94f84f --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_features.py @@ -0,0 +1,64 @@ +from sklearn.feature_extraction import DictVectorizer + +def test_features(patient_data): + assert patient_data["patients"].get("00004348346").get_features() is None + + nr_not_none = 0 + for patient in patient_data["patients"].values(): + if patient.get_features() is not None: + nr_not_none += 1 + + features = patient.get_features() + + assert type(features.get("age", None)).__name__=="int" + assert 0 <= features.get("age") < 150 + + assert type(features.get("length_of_stay")).__name__=="int" + assert 0 <= features.get("length_of_stay") + + assert type(features.get("surgery")).__name__ == "bool" + + assert type(features.get("icu")).__name__ == "bool" + + assert type(features.get("plz")).__name__ == "str" + assert type(features.get("kanton")).__name__ == "str" + + assert type(features.get("language")).__name__ == "str" + + assert nr_not_none == 2 + +def test_features_antibiotic_exposure(patient_data): + features = patient_data["patients"].get("00008301433").get_features() + assert features["antibiotic=J01DC02"] == 2 + +def test_features_chop_codes(patient_data): + features = patient_data["patients"].get("00008301433").get_features() + assert features["chop=Z99"] + +def test_features_rooms(patient_data): + features = patient_data["patients"].get("00008301433").get_features() + assert features["room=BH N 125"] + assert features["room=KARR EKG"] + +def test_features_devices(patient_data): + features = patient_data["patients"].get("00008301433").get_features() + assert features["device=ECC"] + +def test_features_employees(patient_data): + features = patient_data["patients"].get("00008301433").get_features() + assert features["employee=0030236"] == 203 # from RAP + assert features["employee=0324009"] == 43 # from TACS + +def test_dict_vectorizer(patient_data): + risk_factors = [] + for patient in patient_data["patients"].values(): + p_risk_factors = patient.get_features() + if p_risk_factors is not None: + risk_factors.append(p_risk_factors) + v = DictVectorizer(sparse=False) + features = v.fit_transform(risk_factors) + + assert len(features) == 2 + assert len(features[0]) == 203 + + assert "device=ECC" in v.vocabulary_ diff --git a/spitalhygiene/vre/src/main/python/tests/test_icu_stay.py b/spitalhygiene/vre/src/main/python/tests/test_icu_stay.py new file mode 100644 index 0000000..68fb3b5 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_icu_stay.py @@ -0,0 +1,5 @@ + +def test_has_icu_stay(patient_data): + icu_orgs = ["N NORD"] + assert(patient_data["patients"].get("00008301433").has_icu_stay(icu_orgs)) + assert ( not patient_data["patients"].get("00003067149").has_icu_stay(icu_orgs)) \ No newline at end of file diff --git a/spitalhygiene/vre/src/main/python/tests/test_idc_codes.py b/spitalhygiene/vre/src/main/python/tests/test_idc_codes.py new file mode 100644 index 0000000..bb38d49 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_idc_codes.py @@ -0,0 +1,17 @@ + +def test_icd_code(patient_data): + """ + Test ICD codes for case "0006148746" + + :param patient_data: Dictionary containing all VRE-relevant information (see @pytest.fixture patient_data() for details) + """ + icd_list = [each_icd.icd_code for each_icd in patient_data['cases'].get('0006148746').icd_codes] + + # This patient should have 5 ICD codes: C22.0, B16.9, G47.31, J45.9 and Z00.6 + + assert 'C22.0' in icd_list + assert 'B16.9' in icd_list + assert 'G47.31' in icd_list + assert 'J45.9' in icd_list + assert 'Z00.6' in icd_list + diff --git a/spitalhygiene/vre/src/main/python/tests/test_label.py b/spitalhygiene/vre/src/main/python/tests/test_label.py new file mode 100644 index 0000000..6c45d9c --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_label.py @@ -0,0 +1,7 @@ +def test_label_patient(patient_data): + # no relevant case + assert patient_data["patients"].get("00004348346").get_label() == -1 + # screening removed + assert patient_data["patients"].get("00003067149").get_label() == 2 + # screening unconclusive: 00008301433 + assert patient_data["patients"].get("00008301433").get_label() == 1 diff --git a/spitalhygiene/vre/src/main/python/tests/test_load_data.py b/spitalhygiene/vre/src/main/python/tests/test_load_data.py new file mode 100644 index 0000000..34b38b2 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_load_data.py @@ -0,0 +1,56 @@ +import os +import sys +sys.path.append('../vre/model') +import pytest +import configparser + +from Patient import Patient +from Case import Case +from Move import Move +from Risk import Risk + +from conftest import get_hdfs_pipe + +# Load configuration file +config_reader = configparser.ConfigParser() +config_reader.read('../vre/BasicConfig.ini') + +base_path = config_reader['PATHS']['test_data_dir'] + +patients_path = os.path.join(base_path, "V_DH_DIM_PATIENT_CUR.csv") +cases_path = os.path.join(base_path, "V_LA_ISH_NFAL_NORM.csv") +moves_path = os.path.join(base_path, "LA_ISH_NBEW.csv") + + +def test_load_patients(): + pytest.patients = dict() + pytest.cases = dict() + pytest.rooms = dict() + pytest.wards = dict() + pytest.partners = dict() + + pytest.patients = Patient.create_patient_dict(get_hdfs_pipe(patients_path)) + assert len(pytest.patients) == 3 + + +def test_load_cases(): + pytest.cases = Case.create_case_map(get_hdfs_pipe(cases_path), pytest.patients) + assert len(pytest.cases) == 31 + assert pytest.cases.get("0006314210", None) is not None + assert pytest.patients.get("00003067149", None) is not None + + +def test_load_moves(): + Move.add_move_to_case( + get_hdfs_pipe(moves_path), + pytest.cases, + pytest.rooms, + pytest.wards, + pytest.partners, + ) + assert len(pytest.patients.get("00003067149").cases.get("0006314210").moves) == 12 + assert len(pytest.rooms) == 15 + assert len(pytest.wards) == 26 + assert pytest.rooms.get("BH H 116", None) is not None + assert pytest.wards.get("INEGE 2", None) is not None + assert len(pytest.rooms.get("BH H 116").moves) == 3 diff --git a/spitalhygiene/vre/src/main/python/tests/test_model_creator.py b/spitalhygiene/vre/src/main/python/tests/test_model_creator.py new file mode 100644 index 0000000..cc00769 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_model_creator.py @@ -0,0 +1,53 @@ +import sys +sys.path.append('../vre') + +from feature_extractor import feature_extractor + +import pandas as pd + + +def test_prepare_features_and_labels(patient_data): + mc = feature_extractor() + features, labels, dates, v = mc.prepare_features_and_labels( + patient_data["patients"] + ) + + assert len(features) == len(labels) == len(dates) == 2 + assert labels[1] == 2 + + +def test_export_data(patient_data, tmpdir_factory): + mc = feature_extractor() + features, labels, dates, v = mc.prepare_features_and_labels( patient_data["patients"] ) + + fn = tmpdir_factory.mktemp("data").join("risk_factors.csv") + + mc.export_csv(features, labels, dates, v, str(fn)) + + result_dataframe = pd.read_csv(str(fn)) + + assert not result_dataframe.empty + assert result_dataframe["age"].sum() == 144 + assert result_dataframe["device=ECC"].sum() == 1 + assert result_dataframe["label"].sum() == 3 + + +def test_export_gephi(patient_data, tmpdir_factory): + mc = feature_extractor() + features, labels, dates, v = mc.prepare_features_and_labels( patient_data["patients"] ) + + print(features) + print(v.feature_names_) + + file_dir = tmpdir_factory.mktemp("data") # creates a temporary directory only for this test session + + mc.export_gephi(features, labels, dates, v, file_dir) + + result_nodes = pd.read_csv(file_dir.join("node_list.csv")) + result_edges = pd.read_csv(file_dir.join("edge_list.csv")) + + assert result_nodes.shape == (2, 4) + assert result_edges.shape == (2, 5) + + assert result_nodes["Category"][0] == 1 + assert result_nodes["Category"][1] == 2 diff --git a/spitalhygiene/vre/src/main/python/tests/test_nr_cases.py b/spitalhygiene/vre/src/main/python/tests/test_nr_cases.py new file mode 100644 index 0000000..85ffc7c --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_nr_cases.py @@ -0,0 +1,2 @@ +def test_nr_cases(patient_data): + assert(patient_data["patients"].get("00003067149").get_nr_cases() == 13) \ No newline at end of file diff --git a/spitalhygiene/vre/src/main/python/tests/test_partners.py b/spitalhygiene/vre/src/main/python/tests/test_partners.py new file mode 100644 index 0000000..9ed4b7d --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_partners.py @@ -0,0 +1,7 @@ +def test_partner_dict(patient_data): + assert len(patient_data["partners"]) == 1 + +def test_partner_case(patient_data): + assert len(patient_data["patients"].get("00008301433").get_partner()) == 1 + assert len(patient_data["patients"].get("00003067149").get_partner()) == 0 + assert patient_data["patients"].get("00004348346").get_partner() is None \ No newline at end of file diff --git a/spitalhygiene/vre/src/main/python/tests/test_relevant_rooms.py b/spitalhygiene/vre/src/main/python/tests/test_relevant_rooms.py new file mode 100644 index 0000000..c9f7d07 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_relevant_rooms.py @@ -0,0 +1,21 @@ +import datetime + +def test_moves_before_dt(patient_data): + assert ( + len( + patient_data["cases"] + .get("0006594482") + .get_moves_before_dt(datetime.datetime(2018, 7, 6, 0, 0)) + ) + == 3 + ) + +def test_relevant_rooms(patient_data): + # From SAP IS-H: BH N 125, INE GE06 + # From RAP: KARR EKG, INO OP 08 + room_names = patient_data["patients"].get("00008301433").get_relevant_rooms() + assert(len(room_names) == 4) + assert("BH N 125" in room_names) + assert("INE GE06" in room_names) + assert("INO OP 08" in room_names) + assert("KARR EKG" in room_names) diff --git a/spitalhygiene/vre/src/main/python/tests/test_risk_data.py b/spitalhygiene/vre/src/main/python/tests/test_risk_data.py new file mode 100644 index 0000000..52d894d --- /dev/null +++ b/spitalhygiene/vre/src/main/python/tests/test_risk_data.py @@ -0,0 +1,33 @@ +import datetime + + +def test_risk_codes(patient_data): + nr_risk = 0 + for patient in patient_data["patients"].values(): + if patient.has_risk([(32, None), (42, None), (22, None)]): + nr_risk += 1 + assert nr_risk == 1 + + +def test_load_risk_deleted(patient_data): + assert patient_data["patients"].get("00003067149").has_risk([(142, None)]) + + +def test_risk_date(patient_data): + # screening date + assert patient_data["patients"].get("00008301433").get_risk_date( + [(42, None)] + ) == datetime.datetime(2018, 7, 9, 16, 32, 10) + + # screening date of deleted screening + assert patient_data["patients"].get("00003067149").get_risk_date( + [(142, None)] + ) == datetime.datetime(2018, 3, 16, 0, 0, 0) + + +def test_risk_case(patient_data): + # do we get the relevant case for patients with risk factors (32: positive, 42: screening, 142: screening deleted)? + assert patient_data["patients"].get("00003067149").get_relevant_case() is not None + + # patient with no relevant risk and only old cases + assert patient_data["patients"].get("00004348346").get_relevant_case() is None diff --git a/spitalhygiene/vre/src/main/python/vre/BasicConfig.ini b/spitalhygiene/vre/src/main/python/vre/BasicConfig.ini new file mode 100644 index 0000000..5ac0aa4 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/BasicConfig.ini @@ -0,0 +1,40 @@ +[PATHS] +# Path to the "vre_input" folder +input_dir = /home/i0308559/vre_input + +# Path to the "vre_output" folder +output_dir = /home/i0308559/vre_output + +# Path to directory containing test csv files +test_data_dir = /home/i0308559/vre_input/test_data + +# Path to directory containing complete csv data files for model +model_data_dir = /home/i0308559/vre_input/model_data + +# absolute file path for the exported feature vector CSV file +csv_export_path = /home/i0308559/vre_output/feature_vector/feature_vector.csv + +# path to directory in which edge_list.csv and node_list.csv for import into Gephi will be saved +gephi_export_dir = /home/i0308559/vre_output/gephi + +# path to Theus' JAR file +jar_file_path = /data1/sqooba/vre/jar_files/spitalhygiene-2.0-SNAPSHOT-jar-with-dependencies.jar + +# directory containing all log files +log_dir = /home/i0308559/vre_log + +# directory into which all Neo4J data will be exported +neo4j_dir = /home/i0308559/vre_output/neo4j + +# directory containing the odbc connection files (see README for structure) +odbc_file_path = /home/i0308559/connection_file/server_connection.txt + +[DELIMITERS] +# delimiter used for CSV files (default is ,) +csv_sep = , + + +[PARAMETERS] +# Indicator for data to use, one of 'test' (will load test patient only) or 'all' (data for all patients) +data_basis = all + diff --git a/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py b/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py new file mode 100644 index 0000000..8e0bfaa --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py @@ -0,0 +1,283 @@ +# -*- coding: utf-8 -*- +"""This script contains all functions for loading data from CSV or HDFS, and controls the creation of all +objects required for the VRE model. + +----- +""" + +import subprocess +import csv +import os +import logging +import sys +import configparser + +# make sure to append the correct path regardless where script is called from +sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'model')) + +from Patient import Patient +from Risk import Risk +from Case import Case +from Room import Room +from Move import Move +from Medication import Medication +from Appointment import Appointment +from Device import Device +from Employee import Employee +from Chop import Chop +from Surgery import Surgery +from Partner import Partner +from Care import Care +from ICD import ICD + +############################################################################################################### + + +class HDFS_data_loader: + """Loads all the csv files from HDFS and creates the data model. + """ + + def __init__(self, hdfs_pipe=True): + # Load configuration file + config_reader = configparser.ConfigParser() + config_reader.read(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'BasicConfig.ini')) + + self.load_test_data = config_reader['PARAMETERS']['data_basis'] == 'test' + + self.base_path = config_reader['PATHS']['model_data_dir'] if self.load_test_data is False \ + else config_reader['PATHS']['test_data_dir'] + + logging.debug(f"base_path: {self.base_path}") + + self.devices_path = os.path.join(self.base_path, "V_DH_DIM_GERAET_CUR.csv") + self.patients_path = os.path.join(self.base_path, "V_DH_DIM_PATIENT_CUR.csv") + self.cases_path = os.path.join(self.base_path, "V_LA_ISH_NFAL_NORM.csv") + self.moves_path = os.path.join(self.base_path, "LA_ISH_NBEW.csv") + self.risks_path = os.path.join(self.base_path, "V_LA_ISH_NRSF_NORM.csv") + # self.deleted_risks_path = os.path.join(self.base_path, "deleted_screenings.csv") + self.appointments_path = os.path.join(self.base_path, "V_DH_DIM_TERMIN_CUR.csv") + self.device_appointment_path = os.path.join(self.base_path, "V_DH_FACT_TERMINGERAET.csv") + self.appointment_patient_path = os.path.join(self.base_path, "V_DH_FACT_TERMINPATIENT.csv") + self.rooms_path = os.path.join(self.base_path, "V_DH_DIM_RAUM_CUR.csv") + self.room_appointment_path = os.path.join(self.base_path, "V_DH_FACT_TERMINRAUM.csv") + self.appointment_employee_path = os.path.join(self.base_path, "V_DH_FACT_TERMINMITARBEITER.csv") + self.medication_path = os.path.join(self.base_path, "V_LA_IPD_DRUG_NORM.csv") + self.bwart_path = os.path.join(self.base_path, "BWTYP-BWART.csv") + self.partner_path = os.path.join(self.base_path, "LA_ISH_NGPA.csv") + self.partner_case_path = os.path.join(self.base_path, "LA_ISH_NFPZ.csv") + self.chop_path = os.path.join(self.base_path, "V_DH_REF_CHOP.csv") + self.surgery_path = os.path.join(self.base_path, "LA_ISH_NICP.csv") + self.tacs_path = os.path.join(self.base_path, "TACS_DATEN.csv") + self.icd_path = os.path.join(self.base_path, "LA_ISH_NDIA_NORM.csv") + self.VRE_screenings_path = os.path.join(self.base_path, "VRE_Screenings_Final.csv") + + self.hdfs_pipe = hdfs_pipe # binary attribute specifying whether to read data Hadoop (True) or CSV (False) + + self.file_delim = config_reader['DELIMITERS']['csv_sep'] # delimiter character for reading CSV files + + def get_hdfs_pipe(self, path): + """Loads a datafile from HDFS. + + Loads the datafile specified in path from the Hadoop file system, and returns the file **without header** as a + csv.reader() instance. This function is used in the method patient_data() if hdfs_pipe is ``True`` + (the default). + + Args: + path (str): full path to file in HDFS to be loaded. + + Returns: + ``csv.reader()`` instance **not** containing the header of the file. + """ + logging.debug(f"get_hdfs_pipe: {path}") + encoding = "iso-8859-1" + cat = subprocess.Popen(["hadoop", "fs", "-cat", path], stdout=subprocess.PIPE) + output = cat.communicate()[0].decode(encoding) + lines = csv.reader(output.splitlines(), delimiter=self.file_delim) + next(lines, None) # skip header + return lines + + def get_csv_file(self, csv_path): + """Loads a datafile from CSV. + + Loads the datafile specified in csv_path, and returns the file **without header** as a csv.reader() instance. + ``csv_path`` must be an **absolute** filepath. This function is used in the method patient_data() if hdfs_pipe + is ``False``. + + Args: + csv_path (str): full path to csv file. + + Important: + Since the csv.reader() instance is returned by this functions via ``open(csv_path, ...)``, these files may + not be properly closed ! + + Returns: + ``csv.reader()`` instance **not** containing the header of the file. + """ + logging.debug(f"csv_path: {csv_path}") + encoding = "iso-8859-1" + output = csv.reader(open(csv_path, 'r', encoding=encoding), delimiter=self.file_delim) + # Note --> Test Data are ';'-delimited, but original data are ','-delimited ! + next(output, None) # ignore the header line + return output + + def patient_data(self, risk_only=False): + """Prepares patient data based on all results obtained from the SQL queries. + + If self.hdfs_pipe is ``True``, this will use the :meth:`get_hdfs_pipe()` method. Otherwise, the + :meth:`get_csv_file()` method is used. + + Args: + risk_only (bool): Whether or not to use only risk data (defaults to ``False``). + + Returns: + dict: Dictionary containing all VRE-relevant objects of the form + + { "rooms" :math:`\\longrightarrow` *Rooms*, + + "wards" :math:`\\longrightarrow` *Wards*, etc. } + + Please refer to the ``vre/src/main/python/vre/model`` folder documentation for more details. + """ + rooms = dict() # dictionary mapping room names to Room() objects --> {'BH N 123' : Room(), ... } + wards = dict() + + logging.info(f"Processing patient data (load_test_data is {self.load_test_data}, hdfs_pipe is {self.hdfs_pipe}," + f" base_path set to {self.base_path}).") + + # Load Patient data from table: V_DH_DIM_PATIENT_CUR + logging.info("loading patient data") + patients = Patient.create_patient_dict(self.get_hdfs_pipe(self.patients_path) if self.hdfs_pipe is True + else self.get_csv_file(self.patients_path)) + + # Load Case data from table: V_LA_ISH_NFAL_NORM + logging.info("loading case data") + cases = Case.create_case_map(self.get_hdfs_pipe(self.cases_path) if self.hdfs_pipe is True + else self.get_csv_file(self.cases_path), patients) + + # Load Partner data from table: LA_ISH_NGPA + partners = Partner.create_partner_map(self.get_hdfs_pipe(self.partner_path) if self.hdfs_pipe is True + else self.get_csv_file(self.partner_path)) + Partner.add_partners_to_cases( # This will update partners from table: LA_ISH_NFPZ + self.get_hdfs_pipe(self.partner_case_path) if self.hdfs_pipe is True + else self.get_csv_file(self.partner_case_path), cases, partners) + + # Load Move data from table: LA_ISH_NBEW + logging.info("loading move data") + Move.add_move_to_case(self.get_hdfs_pipe(self.moves_path) if self.hdfs_pipe is True + else self.get_csv_file(self.moves_path), cases, rooms, wards, partners) + # --> Note: Move() objects are not part of the returned dictionary, they are only used in + # Case() objects --> Case().moves = [1 : Move(), 2 : Move(), ...] + + # ---------------------------------------------------------------- + # Load Risk data --> ADJUST THIS SECTION ! + logging.info("loading risk data") + # ## --> OLD VERSION: from table: V_LA_ISH_NRSF_NORM + # Risk.add_risk_to_patient( self.get_hdfs_pipe(self.risks_path) if self.hdfs_pipe is True + # else self.get_csv_file(self.risks_path), patients ) + # Risk.add_deleted_risk_to_patient( # Update data from table: deleted_screenings + # self.get_hdfs_pipe(self.deleted_risks_path) if self.hdfs_pipe is True + # else self.get_csv_file(self.deleted_risks_path), patients + # ) + # ## --> NEW VERSION: from file VRE_Screenings_Final.csv + Risk.add_screening_data_to_patients(lines=self.get_hdfs_pipe(self.VRE_screenings_path) + if self.hdfs_pipe is True + else self.get_csv_file(self.VRE_screenings_path), patient_dict=patients) + + if risk_only: + logging.info("keeping only risk patients") + patients_risk = dict() + for patient in patients.values(): + if patient.get_label() > 0: + patients_risk[patient.patient_id] = patient + patients = patients_risk + logging.info(f"{len(patients)} patients") + # ---------------------------------------------------------------- + + # Load Drug data from table: V_LA_IPD_DRUG_NORM + logging.info("loading drug data") + drugs = Medication.create_drug_map(self.get_hdfs_pipe(self.medication_path) if self.hdfs_pipe is True + else self.get_csv_file(self.medication_path)) + Medication.add_medication_to_case( # Update is based on the same table + self.get_hdfs_pipe(self.medication_path) if self.hdfs_pipe is True + else self.get_csv_file(self.medication_path), cases) + + # Load CHOP data from table: V_DH_REF_CHOP + logging.info("loading chop data") + chops = Chop.create_chop_dict(self.get_hdfs_pipe(self.chop_path) if self.hdfs_pipe is True + else self.get_csv_file(self.chop_path)) + + # Add Surgery data to cases from table: LA_ISH_NICP + Surgery.add_surgery_to_case(self.get_hdfs_pipe(self.surgery_path) if self.hdfs_pipe is True + else self.get_csv_file(self.surgery_path), cases, chops) + # Surgery() objects are not part of the returned dictionary + + # Load Appointment data from table: V_DH_DIM_TERMIN_CUR + logging.info("loading appointment data") + appointments = Appointment.create_termin_map(self.get_hdfs_pipe(self.appointments_path) + if self.hdfs_pipe is True + else self.get_csv_file(self.appointments_path)) + + # Add Appointments to cases from table: V_DH_FACT_TERMINPATIENT + logging.info('Adding appointments to cases') + Appointment.add_appointment_to_case(self.get_hdfs_pipe(self.appointment_patient_path) if self.hdfs_pipe is True + else self.get_csv_file(self.appointment_patient_path), + cases, appointments) + + # Load Device data from table: V_DH_DIM_GERAET_CUR + logging.info("loading device data") + devices = Device.create_device_map(self.get_hdfs_pipe(self.devices_path) if self.hdfs_pipe is True + else self.get_csv_file(self.devices_path)) + + # Add Device data to Appointments from table: V_DH_FACT_TERMINGERAET + Device.add_device_to_appointment(self.get_hdfs_pipe(self.device_appointment_path) if self.hdfs_pipe is True + else self.get_csv_file(self.device_appointment_path), + appointments, devices) + + # Load Employee data (RAP) from table: V_DH_FACT_TERMINMITARBEITER + logging.info("loading employee data from RAP") + employees = Employee.create_employee_map(self.get_hdfs_pipe(self.appointment_employee_path) + if self.hdfs_pipe is True + else self.get_csv_file(self.appointment_employee_path)) + + # Add Employees to Appointments using the same table + Employee.add_employee_to_appointment(self.get_hdfs_pipe(self.appointment_employee_path) + if self.hdfs_pipe is True + else self.get_csv_file(self.appointment_employee_path), + appointments, employees) + + # Add Care data to Cases from table: TACS_DATEN + logging.info("Adding Care data to Cases from TACS") + Care.add_care_to_case(self.get_hdfs_pipe(self.tacs_path) if self.hdfs_pipe is True + else self.get_csv_file(self.tacs_path), cases, employees) + # --> Note: Care() objects are not part of the returned dictionary, they are only used in + # Case() objects --> Case().cares = [Care(), Care(), ...] (list of all cares for each case) + + # Add Room data to Appointments from table: V_DH_FACT_TERMINRAUM + logging.info('Adding rooms to appointments') + Room.add_room_to_appointment(self.get_hdfs_pipe(self.room_appointment_path) if self.hdfs_pipe is True + else self.get_csv_file(self.room_appointment_path), appointments, rooms) + logging.info(f"Dataset contains in total {len(rooms)} Rooms") + + # Add ICD codes to cases from table: LA_ISH_NDIA_NORM + icd_codes = ICD.create_icd_dict(self.get_hdfs_pipe(self.icd_path) if self.hdfs_pipe is True + else self.get_csv_file(self.icd_path)) + ICD.add_icd_to_case(self.get_hdfs_pipe(self.icd_path) if self.hdfs_pipe is True + else self.get_csv_file(self.icd_path), cases) + + return dict( + { + "rooms": rooms, + "wards": wards, + "partners": partners, + "patients": patients, + "cases": cases, + "drugs": drugs, + "chops": chops, + "appointments": appointments, + "devices": devices, + "employees": employees, + # "room_id_map": room_id_map, # --> no longer used + 'icd_codes': icd_codes + } + ) + diff --git a/spitalhygiene/vre/src/main/python/vre/Neo4JExporter.py b/spitalhygiene/vre/src/main/python/vre/Neo4JExporter.py new file mode 100644 index 0000000..c9b1d82 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/Neo4JExporter.py @@ -0,0 +1,463 @@ +import configparser +import csv +import datetime +import os +import logging + +class Neo4JExporter: + """ + Class responsible for data export in Neo4J-compatible format. + """ + def __init__(self): + """ + Loads the configuration file and makes its contents available via the self.config attribute (e.g. self.config['PATHS']['some_dir'] + """ + self.config = configparser.ConfigParser() + self.config.read(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'BasicConfig.ini')) # makes configuration file entries available as self.config['PATHS']['XXX'] + + self.init_date = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # timestamp at initiation - used as a suffix for all exported files + logging.basicConfig(format='%(asctime)s - %(levelname)s: %(message)s', level=logging.INFO, datefmt='%d.%m.%Y %H:%M:%S') + + def write_patient(self, patients): + """ + Export patient data for all patients with a relevant case. + + :param patients: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} + """ + written_count = 0 + total_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'patients_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow( + [ + "PATNR:ID", + "type:LABEL", + "risk:LABEL", + "risk_datetime:datetime{timezone:Europe/Bern}", + ] ) # Write file header + for key, patient in patients.items(): + total_count += 1 + l = "False" + d = None + if patient.has_risk(): + l = "True" + d = patient.get_risk_date() # returns either a dt.dt() object or None + if patient.get_relevant_case() is not None: # filter for patients with a relevant case + csvwriter.writerow( + [ + patient.patient_id, + "Patient", + l, + d.strftime("%Y-%m-%dT%H:%M") if d is not None else None, + ] ) + written_count += 1 + logging.info(f'Created {written_count} patients with a relevant case out of {total_count} patients.') + + + def write_patient_patient(self, contact_pats, patients): + """ + Export all contacts between patients. + + :param contact_pats: List containing tuples of length 6 of either the format: (source_pat_id, dest_pat_id, start_overlap_dt, end_overlap_dt, room_name, "kontakt_raum") + or the format: (source_pat_id, dest_pat_id, start_overlap_dt, end_overlap_dt, ward_name, "kontakt_org") + :param patients: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} + """ + contact_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'patient_patient_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow( + [ + ":START_ID", + ":END_ID", + "from:datetime{timezone:Europe/Bern}", + "to:datetime{timezone:Europe/Bern}", + "room", + ":TYPE", + ] ) + for contact in contact_pats: # list of tuples --> can be directly printed ! + if patients[contact[0]].get_relevant_case() is not None and patients[contact[1]].get_relevant_case() is not None: # make sure to include only patients with a relevant date ! + csvwriter.writerow(contact) + contact_count += 1 + logging.info(f'Created {contact_count} patient contacts.') + + + def write_room(self, rooms): + """ + Export all room data. + + :param rooms: Dictionary mapping room names to a Room() object --> {'BH N 125' : Room(), ... } + """ + room_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'zimmer_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow(["Name:ID", "type:LABEL"]) # header + for k, r in rooms.items(): + csvwriter.writerow([r.name, "Room"]) + room_count += 1 + logging.info(f'Created {room_count} rooms.') + + + def write_patient_room(self, patients): + """ + Export room data only for patients with a relevant case in the sense: patient_id --[in]--> room_name + + :param patients: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} + """ + relcase_count = 0 + patient_count = 0 + room_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'patient_zimmer_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow( + [ + ":START_ID", + ":END_ID", + "from:datetime{timezone:Europe/Bern}", + "to:datetime{timezone:Europe/Bern}", + ":TYPE", + ] ) # header + for patient in patients.values(): + patient_count += 1 + pat_rel_case = patient.get_relevant_case() + if pat_rel_case is not None: + relcase_count += 1 + for move in pat_rel_case.moves.values(): # the Case().moves attribute is a dictionary mapping the order of moves to Moves() objects + if move.room is not None: + csvwriter.writerow( + [ + patient.patient_id, + move.room.name, + move.bwi_dt.strftime("%Y-%m-%dT%H:%M"), + move.bwe_dt.strftime("%Y-%m-%dT%H:%M"), + "in", + ]) + room_count += 1 + logging.info(f'Wrote {room_count} rooms (based on moves) for {relcase_count} patients with relevant cases out of {patient_count} patients.') + + + def write_bed(self, rooms): + """ + Export all beds involved in all rooms. + + :param rooms: Dictionary mapping room names to a Room() object --> {'BH N 125' : Room(), ... } + """ + room_count = 0 + bed_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'bett_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow(["Name:ID", "type:LABEL"]) + for k, r in rooms.items(): + room_count += 1 + for b in r.beds: + bed_count += 1 + csvwriter.writerow([b, "Bed"]) + logging.info(f'Created {bed_count} beds from {room_count} rooms.') + + + def write_room_bed(self, rooms): + """ + Assign beds to rooms. + :param rooms: Dictionary mapping room names to a Room() object --> {'BH N 125' : Room(), ... } + """ + room_count = 0 + bed_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'zimmer_bett_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow([":START_ID", ":END_ID", ":TYPE"]) + for k, r in rooms.items(): + room_count += 1 + for b in r.beds: + csvwriter.writerow([r.name, b, "in"]) + bed_count += 1 + logging.info(f'Assigned {bed_count} beds to {room_count} rooms.') + + + def write_device(self, devices): + """ + Exports all devices. + + :param rooms: Dictionary mapping room names to a Room() object --> {'BH N 125' : Room(), ... } + """ + device_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'geraet_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow([":ID", "type:LABEL", "name"]) + for k, g in devices.items(): + csvwriter.writerow([g.geraet_id, "Device", g.geraet_name]) + device_count += 1 + logging.info(f'Created {device_count} devices.') + + + def write_patient_device(self, patients): + """ + Exports all contacts between patients and a specific "geraet" if the contact happened during the patient's relevant case. + + :param patients: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} + """ + pat_count = 0 + relcase_count = 0 + device_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'patient_geraet_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow( [":START_ID", ":END_ID", ":TYPE", "from:datetime{timezone:Europe/Bern}"] ) + for k, patient in patients.items(): + pat_count += 1 + pat_rel_case = patient.get_relevant_case() + if pat_rel_case is not None: # consider only patients with a relevant case + relcase_count += 1 + for t in pat_rel_case.appointments: # consider only appointments in the relevant case + for d in t.devices: + csvwriter.writerow( + [ + patient.patient_id, + d.geraet_id, + "used", + t.termin_datum.strftime("%Y-%m-%dT%H:%M"), + ] ) + device_count += 1 + logging.info(f'Added {device_count} devices for {relcase_count} cases from {pat_count} patients.') + + def write_drug(self, drugs): + """ + Export all drugs. + + :param drugs: A dictionary mapping drug codes to their respective text description --> {'B02BA01' : 'NaCl Braun Inf Lös 0.9 % 500 ml (Natriumchlorid)', ... } + """ + drug_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'drugs_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow([":ID", "type:LABEL", "name"]) + for k, d in drugs.items(): + csvwriter.writerow([k, "Drug", d]) + drug_count += 1 + logging.info(f'Created {drug_count} drugs.') + + + def write_patient_medication(self, patients): + """ + Export exposure to drugs for all patients with a relevant case. + + :param patients: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} + """ + pat_count = 0 + exposure_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'patient_medication_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow( + [ + ":START_ID", + ":END_ID", + ":TYPE", + "from:datetime{timezone:Europe/Bern}", + "to:datetime{timezone:Europe/Bern}", + ] ) # header + for k, p in patients.items(): + pat_count += 1 + exposure = p.get_antibiotic_exposure() # this will return None if the patient has no relevant case + if exposure is None: + continue + for k, d in exposure.items(): # d is a set of datetime.date() objects + date_list = list(d) + min_date = date_list[date_list.index(min(date_list))] + max_date = date_list[date_list.index(max(date_list))] + csvwriter.writerow( + [ + p.patient_id, + k, + "administered", + min_date.strftime("%Y-%m-%dT%H:%M"), + max_date.strftime("%Y-%m-%dT%H:%M"), + ] ) + exposure_count += 1 + logging.info(f'Created {exposure_count} exposures from {pat_count} patients.') + + + def write_employees(self, employees): + """ + Export all employees. + + :param employees: Dictionary mapping employee_ids to Employee() objects --> {'0032719' : Employee(), ... } + """ + employee_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'employees_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow([":ID", "type:LABEL", "name"]) + for k, e in employees.items(): + csvwriter.writerow([k, "Employee", k]) + employee_count += 1 + logging.info(f'Created {employee_count} employees.') + + def write_patient_employee(self, patients): + """ + Export all contacts between an employee and a patient during the patient's relevant case. + + :param patients: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} + """ + pat_count = 0 + relcase_count = 0 + contact_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'patient_employee_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow( [":START_ID", ":END_ID", ":TYPE", "from:datetime{timezone:Europe/Bern}"] ) + for k, p in patients.items(): + pat_rel_case = p.get_relevant_case() # returns None if patient has no relevant case + pat_count += 1 + if pat_rel_case is not None: + relcase_count += 1 + for t in pat_rel_case.appointments: + for e in t.employees: + if e.mitarbeiter_id != "-1": # indicates an unknown mitarbeiter - these cases are ignored + csvwriter.writerow( + [ + p.patient_id, + e.mitarbeiter_id, + "appointment_with", + t.termin_datum.strftime("%Y-%m-%dT%H:%M"), + ] ) + contact_count += 1 + logging.info(f'Created {contact_count} contacts in {relcase_count} relevant cases from {pat_count} patients.') + + + def write_referrer(self, partners): + """ + Export all referrers. + + :param partners: Dictionary mapping partners to Partner() objects --> {'1001503842' : Partner(), '1001503845' : Partner(), ... } + """ + refer_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'referrers_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow( + [ + ":ID", + "type:LABEL", + "name1", + "name2", + "name3", + "land", + "plz", + "ort", + "ort2", + ] ) + for partner in partners.values(): + csvwriter.writerow( + [ + partner.gp_art, + "Referrer", + partner.name1, + partner.name2, + partner.name3, + partner.land, + partner.pstlz, + partner.ort, + partner.ort2, + ] ) + refer_count += 1 + logging.info(f'Created {refer_count} referrers.') + + + def write_referrer_patient(self, patients): + """ + Export referrers which came into contact with patients during their relevant case. + + :param patients: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} + """ + patient_count = 0 + referrer_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'referrer_patient_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow( [":END_ID", ":START_ID", ":TYPE", "from:datetime{timezone:Europe/Bern}"] ) + for patient in patients.values(): + patient_count += 1 + patient_relcase = patient.get_relevant_case() + if patient_relcase is not None: + if patient_relcase.moves_start is not None: + for referrer in patient_relcase.referrers: + csvwriter.writerow( + [ + patient.patient_id, + referrer.gp_art, + "referring", + patient_relcase.moves_start.strftime("%Y-%m-%dT%H:%M"), + ] ) + referrer_count += 1 + logging.info(f'Created {referrer_count} referrals from {patient_count} patients.') + + + def write_chop_code(self, chops): + """ + Export all chop codes. + + :param chops: Dictionary mapping the chopcode_katalogid entries to Chop() obects --> { 'Z39.61.10_11': Chop(), ... } + """ + chop_count = 0 + filtered_chops = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'chops_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow( + [ + ":ID", + "type:LABEL", + "level1", + "level2", + "level3", + "level4", + "level5", + "level6", + "latest_catalog", + ] ) + # Extract only the latest CHOP codes, given by the highest value in each chop.chop_sap_katalog_id() value + chop_dict = {} # maps chop codes (without the SAP katalog number) to tuples of (SAP_Katalog_ID, chop object) + for each_chop in chops.values(): + chop_count += 1 + if each_chop.chop_code not in chop_dict.keys(): + chop_dict[each_chop.chop_code] = (int(each_chop.chop_sap_katalog_id), each_chop) + else: # indicates that the CHOP code has already been appended + if int(each_chop.chop_sap_katalog_id) > chop_dict[each_chop.chop_code][0]: + chop_dict[each_chop.chop_code] = (int(each_chop.chop_sap_katalog_id), each_chop) # overwrite the previous entry with the "more current" katalog ID + # for key, value in cho + # final_chop_list = [] # holds lists of values to be appended + for chop_tuple in chop_dict.values(): + csvwriter.writerow( + [ + chop_tuple[1].chop_code, + "CHOP", + chop_tuple[1].chop_level1.replace('\n', ' '), # Note that some of the descriptions may contain newline characters + chop_tuple[1].chop_level2.replace('\n', ' '), + chop_tuple[1].chop_level3.replace('\n', ' '), + chop_tuple[1].chop_level4.replace('\n', ' '), + chop_tuple[1].chop_level5.replace('\n', ' '), + chop_tuple[1].chop_level6.replace('\n', ' '), + chop_tuple[1].chop_sap_katalog_id.replace('\n', ' ') + ] ) + filtered_chops += 1 + logging.info(f'Created {filtered_chops} latest CHOP codes out of {chop_count} total CHOP codes.') + + def write_chop_patient(self, patients): + """ + Export CHOP codes for patients during their relevant case. + + :param patients: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} + """ + pat_count = 0 + relcase_count = 0 + chop_count = 0 + with open(os.path.join(self.config['PATHS']['neo4j_dir'], 'chop_patient_' + self.init_date + '.csv'), "w") as csvfile: + csvwriter = csv.writer( csvfile, delimiter=self.config['DELIMITERS']['csv_sep'], quotechar='"', quoting=csv.QUOTE_MINIMAL ) + csvwriter.writerow( [":START_ID", ":END_ID", ":TYPE", "from:datetime{timezone:Europe/Bern}"] ) + for patient in patients.values(): + pat_count += 1 + pat_relcase = patient.get_relevant_case() + if pat_relcase is not None: + relcase_count += 1 + for surgery in pat_relcase.surgeries: + csvwriter.writerow( + [ + patient.patient_id, + surgery.chop.chop_code, + "surgery", + surgery.bgd_op.strftime("%Y-%m-%dT%H:%M"), + ] ) + chop_count += 1 + logging.info(f'Created {chop_count} surgeries (CHOP codes) for {relcase_count} relevant cases from {pat_count} patients.') + diff --git a/spitalhygiene/vre/src/main/python/vre/README.md b/spitalhygiene/vre/src/main/python/vre/README.md new file mode 100644 index 0000000..f891ca1 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/README.md @@ -0,0 +1,91 @@ +# Project Spitalhygiene - Details on Model Creation + +This file contains additional information on the procedures involved in the Spitalhygiene project, including + +1. Querying of data in the Data_Science_Atelier and its export to CSV +2. Import of CSV data into the model +3. Creation of the feature vectors for each patient +4. Export of feature vectors into CSV (serves as basis for Andrew Atkinson's univariate and multivariate analysis) +5. Export of data in Neo4J-compatible format +6. Import into the Neo4J database + +All the above steps are controlled and logged by the file `~/resources/Update_Model.sh`. + +Note: `~` in this script refers to the `spitalhygiene` folder in the master branch of the insel mono-repo on Bitbucket. + +### 1. Querying of data in the Data_Science_Atelier and its export to CSV + +This step is controlled by the `~/resources/Query_Aterlier_Data.py`. This file controls the querying of data from the Aterlier_DataScience via the `pyodbc` module, and +requires that ODBC Driver 17 for SQL Server is installed. In addition, a file containing connection information is required and must be formatted as follows: + +``` +DRIVER={ODBC Driver 17 for SQL Server} +SERVER=PATH/TO/SERVER +DATABASE=YOUR_DATABASE +UID=YOUR_USERNAME +PWD=PASSWORD +``` + +This file is **not** part of this branch and must be available separately, so as not to hard-code SQL connection information in the repo. + +The `Query_Atelier_Data.py` script essentially performs an easy task: it connects to the Data_Science_Atelier, executes a series of SQL queries, +and saves the retrieved data in CSV format to a specified folder. All executed queries are found in the `~/sql` folder. Created CSV files are named identically to the SQL query +scripts, i.e. `LA_ISH_NICP.sql` → `LA_ISH_NICP.csv`. + +### 2. Import of CSV data into VRE model + +The CSV import and model creation is controlled by the `~/vre/src/main/python/vre/feature_extractor.py` script. Data are loaded in multiple steps: + +1. An HDFS_data_loader() instance is created, responsible for the extraction of data from the loaded CSV files +2. Patient data are extracted and loaded via a call to the HDFS_data_loader.patient_data() function +3. Features and labels are processed using an instance of the features_extractor() class +4. Features and labels are prepared via a call to features_extractor().prepare_features_and_labels() + +### 3. Creation of the feature vectors for each patient + +This step is also controlled by the `~/vre/src/main/python/vre/feature_extractor.py` script. + +1. Feature vectors are created based on the HDFS_data_loader.patient_data() object from step 2. +2. Features and labels are processed using an instance of the features_extractor() class and +3. prepared via a call to features_extractor().prepare_features_and_labels() + +### 4. Export of feature vectors into CSV + +Created feature vectors from step 3 are exported as CSV using a call to the features_extractor().export_csv() method. This file is formatted as follows: + +|Patient_ID|Label|age|antibiotic=J01AA02|chop=Z95|employee=0081218|device=Oxy 929992|…| +|---|---|---|---|---|---|---|---| +|ID01|32|68|0|0|0|1| +|ID02|42|50|0|1|0|0| +|ID03|32|65|0|0|30|0| +|ID04|142|61|1|0|0|1| + +Patient_IDs and labels are actually supplied as separate vectors to the model. Important to note is that all contacts are converted into a one-of-k kind of table, meaning that there is a +boolean feature for each "entity" in the data indicating whether or not a patient came into contact with it *at any point during the relevant case*. The only exception are continuous +variables (such as age) and contacts with employees, for which the table lists the *exposure time in minutes* between a particular patient and an employee *in the relevant case*. +Contacts are not restricted to patients, but can be much more diverse: + +- Patient-Bed +- Patient-Device +- Patient-Employee +- Patient-Antibiotic (i.e. if the patient received a particular medicine) +- Patient-CHOP Code (i.e. if a patient underwent a particular surgery) +- etc. + +This "spectrum" of contact possibilities is what makes this approach much more sophisticated than the current approach in the clinics, in which the VRE screening method is merely based +on Patient-Patient interactions over the last 7 days. + +This feature vector file, which is exported as a simple CSV file, also serves as the basis for Andrew Atkinson's univariate and multivariate analysis. + +### 5. Export of data in Neo4J-compatible format + +This part is not yet implemented. + +### 6. Import into the Neo4J database + +This part is not yet implemented. + +----- + +The file `BasicConfig.ini` is used by all modules and intended to simplify the adjustment of paths, settings, etc. when working on different file systems. + diff --git a/spitalhygiene/vre/src/main/python/vre/data_compiler.py b/spitalhygiene/vre/src/main/python/vre/data_compiler.py new file mode 100644 index 0000000..c3ad0d5 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/data_compiler.py @@ -0,0 +1,268 @@ +# -*- coding: utf-8 -*- +"""This script contains all functions for compiling data from CSV or HDFS, and controls the creation of all +objects required for the VRE model. This process has multiple steps and is structured as follows: + +- Reading the ``BasicConfig.ini`` file in this directory +- Loading all VRE-relevant data using the ``HDFS_data_loader`` class +- Creation of the feature vector using the ``feature_extractor`` class +- Export of this feature vector to CSV +- Creation of the surface model using the ``surface_model`` class +- Export of various results from the surface model using its built-in functions + +**This script is called in the cronjob and triggers the build of the VRE model!** + +Please refer to the script code for details. + +----- +""" + +from HDFS_data_loader import HDFS_data_loader +from feature_extractor import feature_extractor +from networkx_graph import surface_model, create_model_snapshots +import preprocessor +import logging +import os +import datetime +import configparser +import calendar +import pathlib + + +if __name__ == "__main__": + ##################################### + # ### Load configuration file + this_filepath = pathlib.Path(os.path.realpath(__file__)).parent + + config_reader = configparser.ConfigParser() + config_reader.read(pathlib.Path(this_filepath, 'BasicConfig.ini')) + + logging.basicConfig(format='%(asctime)s - %(levelname)s: %(message)s', level=logging.INFO, + datefmt='%d.%m.%Y %H:%M:%S') + ##################################### + + ##################################### + # ### Various pre-VRE data processing steps + # 1) Update VRE Screening Overview file for all wards + + ##################################### + + + ##################################### + # ### Initiate data loader + logging.info("Initiating HDFS_data_loader") + + # --> Load all data: + loader = HDFS_data_loader(hdfs_pipe=False) # hdfs_pipe = False --> files will be loaded directly from CSV + patient_data = loader.patient_data() + ##################################### + + ##################################### + # ### Create and export feature vector + logging.info("creating feature vector") + model_creator = feature_extractor() + (features, labels, dates, v) = model_creator.prepare_features_and_labels(patient_data["patients"]) + + # Export feature vector + logging.info("exporting feature vector") + model_creator.export_csv(features, labels, dates, v, config_reader['PATHS']['csv_export_path']) + ##################################### + + ##################################### + # ### Add contact patients to patient_data (CURRENTLY NOT USED, IMPLEMENTED IN NETWORK) + # logging.info('Retrieving patient contacts') + # patient_data['contact_pats'] = model_creator.get_contact_patients(patients=patient_data['patients']) + ##################################### + + ##################################### + # ### Create graph of the CURRENT model in networkX + surface_graph = surface_model(data_dir='/home/i0308559/vre_wd') + surface_graph.add_network_data(patient_dict=patient_data, subset='relevant_case') + surface_graph.remove_isolated_nodes() + surface_graph.add_edge_infection() + + # Extract positive patient nodes + positive_patient_nodes = [node for node, nodedata in surface_graph.S_GRAPH.nodes(data=True) + if nodedata['type'] == 'Patient' and nodedata['vre_status'] == 'pos'] + + # Write node files + surface_graph.write_node_files() + + # Export Patient Degree Ratio + surface_graph.export_patient_degree_ratio(export_path='/home/i0308559/vre_output') + + # Export Total Degree Ratio + surface_graph.export_total_degree_ratio(export_path='/home/i0308559/vre_output') + + # Export node betweenness + surface_graph.update_shortest_path_statistics() + surface_graph.export_node_betweenness(export_path='/home/i0308559/vre_output') + ##################################### + + # Create a monthly TIMESERIES of pdr values starting in January 2018 + # month_snapshots = [datetime.datetime(2018, i, calendar.monthrange(2018, i)[1]) for i in range(1, 13)] # calendar.monthrange() returns a tuple of length 2 of (day_of_week, day_of_month) + # month_snapshots += [datetime.datetime(2019, 1, 31)] # Add values from 2019 + # all_snapshot_models = create_model_snapshots(orig_model = surface_graph, snapshot_dt_list = month_snapshots) + # for model in all_snapshot_models: + # model.export_patient_degree_ratio(export_path='/home/i0308559/vre_output') + ##################################### + + logging.info("Data processed successfully !") + + ##################################### + # ### OBSOLETE SECTION + + + ### Export data in Neo4J-compatible format - REIMPLEMENT THIS PART + # logging.info('Exporting data for Neo4J') + # exporter = Neo4JExporter() + + # Export to csv files for Neo4J - REIMPLEMENT THIS PART + # exporter.write_patient(patient_data["patients"]) + # exporter.write_patient_patient(patient_data['contact_pats'], patient_data['patients']) # patient_data['contact_pats'] is really only a list of tuples of patient contacts + # exporter.write_room(patient_data["rooms"]) + # exporter.write_patient_room(patient_data["patients"]) + # exporter.write_bed(patient_data["rooms"]) + # exporter.write_room_bed(patient_data["rooms"]) + # exporter.write_device(patient_data["devices"]) + # exporter.write_patient_device(patient_data["patients"]) + # exporter.write_drug(patient_data["drugs"]) + # exporter.write_patient_medication(patient_data["patients"]) + # exporter.write_employees(patient_data["employees"]) + # exporter.write_patient_employee(patient_data["patients"]) + # exporter.write_referrer(patient_data["partners"]) + # exporter.write_referrer_patient(patient_data["patients"]) + # exporter.write_chop_code(patient_data["chops"]) + # exporter.write_chop_patient(patient_data["patients"]) + + ########################################################################## + # Export data using the data_purification.py script for manual inspection and purging + ########################################################################## + # relcase_count = 0 + # employee_list = [] + # room_list = [] + # geraet_list = [] + # + # for patient in patient_data['patients'].values(): + # rel_case = patient.get_relevant_case() + # patient_has_risk = patient.has_risk() + # if rel_case is not None: # This filter is important ! + # # Write relevant case and associated patient ID + # dpf.write_patient_case(case = rel_case, has_risk = patient_has_risk) + # relcase_count += 1 + # + # # Note employees associated to the relevant case --> employee_list + # for care in rel_case.cares: + # employee_list.append(care.employee.mitarbeiter_id) + # + # # Note rooms associated to the relevant case --> room_list + # for move in rel_case.moves.values(): + # room_list.append(move.zimmr) + # + # # Note all geraete associated to relevant case --> geraet_list + # for appmnt in rel_case.appointments: + # for each_geraet in appmnt.devices: + # geraet_list.append((str(each_geraet.geraet_id), str(each_geraet.geraet_name))) + # print(f"Wrote {relcase_count} relevant cases and associated patients.") + # + # # Remove duplicate entries from the list and write them to file: + # unique_employees = list(set(employee_list)) + # for each_entry in unique_employees: + # dpf.write_employee(each_entry) + # print(f"Wrote {len(unique_employees)} employees.") + # + # unique_rooms = list(set(room_list)) + # for each_entry in unique_rooms: + # dpf.write_room(each_entry) + # print(f"Wrote {len(unique_rooms)} rooms.") + # + # unique_geraete = list(set(geraet_list)) + # for each_tuple in unique_geraete: + # dpf.write_geraet(each_tuple) + # print(f"Wrote {len(unique_geraete)} devices.") + ########################################################################## + + + ########################################################################## + ### For overview purposes (works only on test data) + ########################################################################## + + # Room object + # print('\nRoom object') + # for attribute in ['name', 'moves', 'appointments', 'beds']: + # print(getattr(patient_data['rooms']['BH N 125'], attribute), type(getattr(patient_data['rooms']['BH N 125'], attribute))) + # + # # Bed object + # print('\nBed object') + # for attribute in ['name', 'moves']: + # print(getattr(patient_data['rooms']['BH N 125'].beds['BHN125F'], attribute), type(getattr(patient_data['rooms']['BH N 125'].beds['BHN125F'], attribute))) + # + # # Moves object + # print('\nMoves object') + # for attribute in ['fal_nr', 'lfd_nr','bew_ty','bw_art','bwi_dt','statu','bwe_dt','ldf_ref','kz_txt','org_fa','org_pf','org_au','zimmr','bett','storn','ext_kh', 'room', 'ward', 'case']: + # print(getattr(patient_data['rooms']['BH N 125'].moves[0], attribute), type(getattr(patient_data['rooms']['BH N 125'].moves[0], attribute))) + # + # # Ward object + # print('\nWard object') + # for attribute in ['name', 'moves', 'appointments']: + # print(getattr(patient_data['rooms']['BH N 125'].moves[0].ward, attribute), type(getattr(patient_data['rooms']['BH N 125'].moves[0].ward, attribute))) + # + # # Case object + # print('\nCase object') + # for attribute in ['patient_id','case_id','case_typ','case_status','fal_ar','beg_dt','end_dt','patient_typ','patient_status','appointments','cares','surgeries','moves','moves_start','moves_end','referrers','patient','medications']: + # print(getattr(patient_data['rooms']['BH N 125'].moves[0].case, attribute), type(getattr(patient_data['rooms']['BH N 125'].moves[0].case, attribute))) + # + # # Patient object + # print('\nPatient object') + # for attribute in ['patient_id','geschlecht','geburtsdatum','plz','wohnort','kanton','sprache','cases','risks']: + # print(getattr(patient_data['rooms']['BH N 125'].moves[0].case.patient, attribute), type(getattr(patient_data['rooms']['BH N 125'].moves[0].case.patient, attribute))) + # + # # Care object + # print('\nCare object') + # for attribute in ['patient_id','case_id','dt','duration_in_minutes','employee_nr','employee']: + # print(getattr(patient_data['rooms']['BH N 125'].moves[0].case.cares[0], attribute), type(getattr(patient_data['rooms']['BH N 125'].moves[0].case.cares[0], attribute))) + # + # # Employee object + # print('\nEmployee object') + # for attribute in ['mitarbeiter_id']: + # print(getattr(patient_data['rooms']['BH N 125'].moves[0].case.cares[0].employee, attribute), type(getattr(patient_data['rooms']['BH N 125'].moves[0].case.cares[0].employee, attribute))) + # + # # Appointment object + # print('\nAppointment object') + # for attribute in ['termin_id','is_deleted','termin_bezeichnung','termin_art','termin_typ','termin_datum','dauer_in_min','devices','employees','rooms']: + # print(getattr(patient_data['rooms']['BH N 125'].moves[0].case.appointments[0], attribute), type(getattr(patient_data['rooms']['BH N 125'].moves[0].case.appointments[0], attribute))) + # + # # Surgery object + # print('\nSurgery object') + # for attribute in ['bgd_op','lfd_bew','icpmk','icpml','anzop','lslok','fall_nr','storn','org_pf','chop']: + # print(getattr(patient_data['rooms']['BH N 125'].moves[0].case.surgeries[0], attribute), type(getattr(patient_data['rooms']['BH N 125'].moves[0].case.surgeries[0], attribute))) + # + # # Chop object + # print('\nChop object') + # for attribute in ['chop_code','chop_verwendungsjahr','chop','chop_code_level1','chop_level1','chop_code_level2','chop_level2','chop_code_level3','chop_level3','chop_code_level4','chop_level4','chop_code_level5','chop_level5','chop_code_level6','chop_level6','chop_status','chop_sap_katalog_id','cases']: + # print(getattr(patient_data['rooms']['BH N 125'].moves[0].case.surgeries[0].chop, attribute), type(getattr(patient_data['rooms']['BH N 125'].moves[0].case.surgeries[0].chop, attribute))) + # + # # Medication object + # print('\nMedication object') + # for attribute in ['patient_id','case_id','drug_text','drug_atc','drug_quantity','drug_unit','drug_dispform','drug_submission']: + # print(getattr(patient_data['rooms']['BH N 125'].moves[0].case.medications[0], attribute), type(getattr(patient_data['rooms']['BH N 125'].moves[0].case.medications[0], attribute))) + # + # # Partner object - these are found in the 'referrers' set attribute, which is why the attribute is converted to a list() + # print('\nPartner object') + # for attribute in ['gp_art','name1','name2','name3','land','pstlz','ort','ort2','stras','krkhs','referred_cases']: + # print(getattr(list(patient_data['rooms']['BH N 125'].moves[0].case.referrers)[0], attribute), type(getattr(list(patient_data['rooms']['BH N 125'].moves[0].case.referrers)[0], attribute))) + # + # # Device object + # print('\nDevice object') + # for attribute in ['geraet_id','geraet_name']: + # print(getattr(patient_data['devices']['64174'], attribute), type(getattr(patient_data['devices']['64174'], attribute))) + # + # # Risk object + # print('\nRisk object --> see class definition') + ########################################################################## + # [END OF FILE] + + + + + + diff --git a/spitalhygiene/vre/src/main/python/vre/feature_extractor.py b/spitalhygiene/vre/src/main/python/vre/feature_extractor.py new file mode 100644 index 0000000..18eb315 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/feature_extractor.py @@ -0,0 +1,287 @@ +# -*- coding: utf-8 -*- +"""This script contains the feature_extractor class, which controls the following aspects of VRE: + +- Preparation of the feature vector +- Extraction of patient-patient contacts +- Export to various sources (Gephi, CSV, Neo4J, etc.) + +----- +""" + +from sklearn.feature_extraction import DictVectorizer +import numpy as np +import pandas as pd +import os +import datetime +from dateutil import relativedelta + + +class feature_extractor: + """Creates pandas dataframes with features, labels and relevant dates, and provides export functions to various + target systems. + """ + @staticmethod + def prepare_features_and_labels(patients): + """*Internal function used in various data exports.* + + Creates the feature ``np.array()`` and label ``np.array()``, along with relevant dates. + + Args: + patients (dict): Dictionary mapping patient ids to Patient() objects of the form + ``{"00001383264": Patient(), "00001383310": Patient(), ...}`` + + Returns: + tuple: tuple of length 4 of the form :math:`\\longrightarrow` *(features, labels, dates, v)* + + Please refer to function code for more details. + """ + risk_factors = [] + labels = [] + dates = [] + for patient in patients.values(): + patient_features = patient.get_features() # Dictionary --> {"length_of_stay" : 47, "nr_cases" : 3, ... } + if patient_features is not None: + risk_factors.append(patient_features) + labels.append(patient.get_label()) # patient.get_label() will return an integer between -1 and 3 + dates.append(patient.get_risk_date()) + # patient.get_risk_date() will return a datetime.datetime() object corresponding to the label risk date + v = DictVectorizer(sparse=False) + features = v.fit_transform(risk_factors) + # features.shape yields a tuple of dimensions in the format (nrow, ncol), + # or a tuple (LENGTH, ) in case of only 1 dimension (i.e. a list) + ############################################################################################################### + # Note: features is a numpy.ndarray() object with one row per patient containing the "fitted" risk factors for + # each patient in the one-of-K fashion as described in the .fit_transform() documentation + # E.g. for a categorical value with levels "a", "b", and "c", it will contain three columns containing 0 or 1 + # and corresponding to value=a, value=b and value=c + ############################################################################################################### + # filter = ~np.isnan(features).any(axis=1) + # features_filtered = features[~np.isnan(features).any(axis=1)] + labels_np = np.asarray(labels) + # --> labels_np is a 1-D numpy.ndarray() object based on labels, which is a list of labels between -1 and 3 + # for each patient in the dataset (e.g. ndarray([1 2 2 -1 3 3 2 1 1 2]) ) + ############################################################################################################### + # labels_np + # labels_filtered = labels_np[filter] + dates_np = np.asarray(dates) + # --> same structure as labels_np, but contains the risk date for each patient in the dataset + # as a datetime.datetime() object + ############################################################################################################### + # dates_filtered = dates_np[filter] + + return features, labels_np, dates_np, v + + @staticmethod + def export_csv(features, labels, dates, v, file_path): + """Function for exporting features, labels and dates to CSV. + + Combines features, labels and dates in a ``pandas.DataFrame()``, and exports all data to the csv file given + in file_path. + + Args: + features (numpy.ndarray()): ``numpy.ndarray()`` with one row per patient containing "fitted" risk factors + for each patient in the one-of-K fashion + labels (numpy.ndarray()): 1-D ``numpy.ndarray()`` containing the labels for each patient + (integers between -1 and 3) + dates (numpy.ndarray()): 1-D ``numpy.ndarray()`` containing risk dates for each patient + v: ``sklearn.feature_extraction.DictVectorizer()`` object with which the *features* + parameter was created + file_path (str): Absolute path to exported csv file + """ + sorted_cols = [k for k in sorted(v.vocabulary_, key=v.vocabulary_.get)] + # --> v.vocabulary_ is a dictionary mapping feature names to feature indices + df = pd.DataFrame(data=features, columns=sorted_cols) + df["label"] = labels + df["diagnosis_date"] = dates + df.to_csv(file_path, sep=",", encoding="utf-8", index=False) + # --> index = False will prevent writing row names in a separate, unlabeled column + + @staticmethod + def export_gephi(features, labels, dates, v, dest_path='.', csv_sep=','): + """Exports the node and edge list in Gephi-compatible format for visualisation. + + Edges and nodes will be exported to files ``dest_path/edge_list.csv`` and ``dest_path/node_list.csv``, + respectively. + + Args: + features (numpy.ndarray()): ``numpy.ndarray()`` with one row per patient containing the "fitted" risk + factors for each patient in the one-of-K fashion. + labels (numpy.ndarray()): 1-D ``numpy.ndarray()`` containing the labels for each patient + (integers between -1 and 3) + dates (numpy.ndarray()): 1-D ``numpy.ndarray()`` containing risk dates for each patient + v: ``sklearn.feature_extraction.DictVectorizer()`` object with which the features + parameter was created + dest_path (str): path into which the edge_list.csv and node_list.csv files will be exported + (defaults to the current working directory) + csv_sep (str): separator for exported csv files (defaults to ``,``) + """ + # --> Process Rooms + room_vocabulary = {key: value for key, value in v.vocabulary_.items() if key.startswith("room")} + # Dictionary of the form --> 'room=INE GE06': 176, 'room=KARR EKG': 183, ...} + + # Maps the feature (i.e. column) names to their respective column index (useful for array slicing later) + room_cols = list(room_vocabulary.values()) # list of indices in v containing room information (for slicing) + + # --> Process Employees + employee_vocabulary = {key: value for key, value in v.vocabulary_.items() if key.startswith("employee")} + # Dictionary of the form --> 'employee=DHGE035': 126, 'employee=0075470': 178, ...} + + # Maps the feature (i.e. column) names to their respective column index (useful for array slicing later) + employee_cols = list(employee_vocabulary.values()) + + # --> Process Devices + device_vocabulary = {key: value for key, value in v.vocabulary_.items() if key.startswith("device")} + # Dictionary of the form --> 'device=ECC': 14, 'device=XY12344': 251, ...} + + # Maps the feature (i.e. column) names to their respective column index (useful for array slicing later) + device_cols = list(device_vocabulary.values()) + + ##################################### + # --> Write EDGE list + ##################################### + edge_list = open(os.path.join(dest_path, 'edge_list.csv'), "w") + edge_list.write(csv_sep.join(['Source', 'Target', 'Weight', 'Type', 'Art\n'])) + nr_patients = len(features) + for ind in range(nr_patients): + if labels.item(ind) >= 1: # only include patients with screening (labels 1,2,3) + for j in range((ind + 1), nr_patients): + if labels.item(j) >= 1: # only include patients with screening (labels 1,2,3) + ##################################### + # --> Process Sources & Targets + ##################################### + # edge goes from older to newer relevant date + source_target_str = str(ind) + csv_sep + str(j) + # Sources and targets are given as integers from 0 to nr_patients - 1 (NOT as patient ids) + if dates[ind] is None or dates[j] is None or dates[ind] > dates[j]: + # indicates that relevant date for patient[j] is older --> switch direction of edge + source_target_str = str(j) + csv_sep + str(ind) + ##################################### + # --> Process shared rooms (used as an edge weight) + ##################################### + weight_rooms = sum(np.logical_and(features[ind, room_cols], features[j, room_cols])) + # logical_and() performs list-wise comparison on multiple lists of the same length and returns + # True if all entries at any position are > 0 and False otherwise + if weight_rooms > 0: + # Only write to edge_list if weight is > 0 - otherwise, this indicates that patients had + # never shared the same room (same applies for all edge weights) + edge_list.write(source_target_str + csv_sep + str(weight_rooms) + csv_sep + "directed" + + csv_sep + "rooms\n") + ##################################### + # --> Process shared employees (used as an edge weight) + ##################################### + weight_employees = sum(np.logical_and(features[ind, employee_cols], features[j, employee_cols])) + if weight_employees > 0: + edge_list.write(source_target_str + csv_sep + str(weight_employees) + csv_sep + 'directed' + + csv_sep + 'employees\n') + ##################################### + # --> Process shared devices (used as an edge weight) + ##################################### + weight_devices = sum( np.logical_and(features[ind, device_cols], features[j, device_cols]) ) + if weight_devices > 0: + edge_list.write(source_target_str + csv_sep + str(weight_devices) + csv_sep + 'directed' + + csv_sep + 'devices\n') + # Close file + edge_list.close() + + ##################################### + # --> Write NODES list + ##################################### + node_list = open(os.path.join(dest_path, 'node_list.csv'), "w") + node_list.write(csv_sep.join(['Id', 'Label', 'Start', 'Category\n'])) + for ind, dt in enumerate(dates): # dates is a vector of either relevant dt.dt() objects or None + infection = "" + if dt is not None: + infection = dt.strftime("%Y-%m-%d") + node_list.write(str(ind) + csv_sep + infection + csv_sep + '0' + csv_sep + str(labels[ind]) + "\n") + + # Close file + node_list.close() + + @staticmethod + def get_contact_patients_for_case(cases, contact_pats): + """Extracts contact patients for specific cases. + + Appends tuples of length 6 (see param contact_pats) directly to contact_pats, which is a list recording all + patient contacts. + + Args: + cases (dict): Dictionary mapping case ids to Case() objects --> {"0003536421" : Case(), + "0003473241" : Case(), ...} + contact_pats (list): List containing tuples of length 6 of either the format: `(source_pat_id, + dest_pat_id, start_overlap_dt, end_overlap_dt, room_name, "kontakt_raum")` in the + case of a contact room, or the format `(source_pat_id, dest_pat_id, + start_overlap_dt, end_overlap_dt, ward_name, "kontakt_org")` in the case of a + contact organization. + """ + for move in cases.moves.values(): + #################################################### + # --> Extract contacts in the same room + #################################################### + if move.bwe_dt is not None and move.room is not None: + overlapping_moves = move.room.get_moves_during(move.bwi_dt, move.bwe_dt) + # get_moves_during() returns a list of all moves overlapping with the .bwi_dt - .bwe_dt interval + for overlap_move in overlapping_moves: + if overlap_move.case is not None and move.case is not None: + if overlap_move.case.fal_ar == "1" and overlap_move.bew_ty in ["1", "2", "3"] and \ + overlap_move.bwe_dt is not None: + start_overlap = max(move.bwi_dt, overlap_move.bwi_dt) + end_overlap = min(move.bwe_dt, overlap_move.bwe_dt) + contact_pats.append( + ( + move.case.patient_id, + overlap_move.case.patient_id, + start_overlap.strftime("%Y-%m-%dT%H:%M"), + end_overlap.strftime("%Y-%m-%dT%H:%M"), + move.room.name, + "kontakt_raum", + )) # append data in the form of a tuple + #################################################### + # --> Extract contacts in the same ward (ORGPF) + #################################################### + if move.bwe_dt is not None and move.ward is not None: + overlapping_moves = move.ward.get_moves_during(move.bwi_dt, move.bwe_dt) + for overlap_move in overlapping_moves: + if overlap_move.case is not None and move.case is not None: + if overlap_move.case.fal_ar == "1" and overlap_move.bew_ty in ["1", "2", "3"] and \ + overlap_move.bwe_dt is not None and \ + (overlap_move.zimmr is None or move.zimmr is None or overlap_move.zimmr != move.zimmr): + start_overlap = max(move.bwi_dt, overlap_move.bwi_dt) + end_overlap = min(move.bwe_dt, overlap_move.bwe_dt) + contact_pats.append( + ( + move.case.patient_id, + overlap_move.case.patient_id, + start_overlap.strftime("%Y-%m-%dT%H:%M"), + end_overlap.strftime("%Y-%m-%dT%H:%M"), + move.ward.name, + "kontakt_org", + )) + + def get_contact_patients(self, patients): + """Extracts all patient contacts. + + Extract all contacts between patients in the same room and same ward which occurred during the last year. + + Args: + patients (dict): Dictionary mapping patient ids to Patient() objects --> {"00001383264" : Patient(), + "00001383310" : Patient(), ...} + + Returns: + list: List containing tuples of length 6 of either the format + + `(source_pat_id, dest_pat_id, start_overlap_dt, end_overlap_dt, room_name, "kontakt_raum")` + + or the format + + `(source_pat_id, dest_pat_id, start_overlap_dt, end_overlap_dt, ward_name, "kontakt_org")` + """ + contact_pats = [] + for pat in patients.values(): + if pat.has_risk(): + for case in pat.cases.values(): + # if case.fal_ar == "1": # only stationary cases + if case.moves_end is not None and case.moves_end > \ + datetime.datetime.now() - relativedelta.relativedelta(years=1): + self.get_contact_patients_for_case(case, contact_pats) + return contact_pats + diff --git a/spitalhygiene/vre/src/main/python/vre/model/Appointment.py b/spitalhygiene/vre/src/main/python/vre/model/Appointment.py new file mode 100644 index 0000000..27ebf17 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Appointment.py @@ -0,0 +1,100 @@ +from datetime import datetime +import logging + + +class Appointment: + """ + Models an appointment from RAP. + Schema: [TERMINID],[IS_DELETED],[TERMINBEZEICHNUNG],[TERMINART],[TERMINTYP],[TERMINDATUM],[DAUERINMIN] + """ + + def __init__( + self, + termin_id, + is_deleted, + termin_bezeichnung, + termin_art, + termin_typ, + termin_datum, + dauer_in_min, + ): + self.termin_id = termin_id + self.is_deleted = is_deleted + self.termin_bezeichnung = termin_bezeichnung + self.termin_art = termin_art + self.termin_typ = termin_typ + self.termin_datum = datetime.strptime(termin_datum, "%Y-%m-%d %H:%M:%S") + try: + self.dauer_in_min = int(float(dauer_in_min)) + except ValueError as e: + self.dauer_in_min = 0 + + self.devices = [] + self.employees = [] + self.rooms = [] + + def add_device(self, device): + self.devices.append(device) + + def add_room(self, room): + self.rooms.append(room) + + def add_employee(self, employee): + self.employees.append(employee) + + def create_termin_map(lines): + """ + Loads the appointments from csv reader. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: V_DH_DIM_TERMIN_CUR + ["TERMINID", "IS_DELETED", "TERMINBEZEICHNUNG", "TERMINART", "TERMINTYP", "TERMINDATUM", "DAUERINMIN"] + ["957219", "0", "K90 HINF", "K90 HINF", "Patiententermin", "2005-02-04 00:00:00.0000", "90.00000"] + ["957224", "0", "Konsultation 15'", "Konsultation 15'", "Patiententermin", "2005-02-03 00:00:00.0000", "15.00000"] + + Returns: A dictionary mapping appointment ids to Appointment() objects --> { '36830543' : Appointment(), ... } + """ + logging.debug("create_termin_map") + nr_malformed = 0 + nr_ok = 0 + appointments = dict() + for line in lines: + if len(line) != 7: + nr_malformed += 1 + continue + else: + appointment = Appointment(*line) + appointments[appointment.termin_id] = appointment + nr_ok += 1 + logging.info(f"{nr_ok} ok, {nr_malformed} appointments malformed") + return appointments + + def add_appointment_to_case(lines, cases, appointments): + """ + Adds Appointment() objects to the SAP cases based on lines read from a csv file. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: V_DH_FACT_TERMINPATIENT + ["TERMINID", "PATIENTID", "FALLID"] + ["35672314", "00008210020", "0005660334"] + ["17255155", "00002042800", "0004017880"] + + :param cases: Dictionary mapping case ids to Case() objects --> {"0003536421" : Case(), "0003473241" : Case(), ...} + :param appointments: Dictionary mapping appointment ids to Appointment() objects --> { '36830543' : Appointment(), ... } + """ + logging.debug("add_appointment_to_case") + nr_appointment_not_found = 0 + nr_case_not_found = 0 + nr_ok = 0 + for line in lines: + appointment_id = line[0] + case_id = line[2] + if appointments.get(appointment_id, None) is None: + nr_appointment_not_found += 1 + continue + if cases.get(case_id, None) is None: + nr_case_not_found += 1 + continue + cases[case_id].add_appointment(appointments[appointment_id]) + nr_ok += 1 + logging.info( + f"{nr_ok} ok, {nr_case_not_found} cases not found, {nr_appointment_not_found} appointments not found" + ) diff --git a/spitalhygiene/vre/src/main/python/vre/model/Bed.py b/spitalhygiene/vre/src/main/python/vre/model/Bed.py new file mode 100644 index 0000000..d9f2702 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Bed.py @@ -0,0 +1,8 @@ +from datetime import datetime + +class Bed: + def __init__(self, name): + self.name = name + self.moves = [] + def add_move(self, move): + self.moves.append(move) \ No newline at end of file diff --git a/spitalhygiene/vre/src/main/python/vre/model/Care.py b/spitalhygiene/vre/src/main/python/vre/model/Care.py new file mode 100644 index 0000000..930bf59 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Care.py @@ -0,0 +1,83 @@ +import logging +from datetime import datetime +from Employee import Employee + + +class Care: + """ + This represents an entry in TACS. + Schema: patient_patientid],[patient_typ],[patient_status],[fall_nummer],[fall_typ],[fall_status],[datum_betreuung],[dauer_betreuung_in_min],[mitarbeiter_personalnummer],[mitarbeiter_anstellungsnummer],[mitarbeiter_login],[BATCH_RUN_ID] + """ + + def __init__( + self, + patient_patientid, + patient_typ, + patient_status, + fall_nummer, + fall_typ, + fall_status, + datum_betreuung, + dauer_betreuung_in_min, + mitarbeiter_personalnummer, + mitarbeiter_anstellungsnummer, + mitarbeiter_login, + batch_run_id, + ): + self.patient_id = patient_patientid + self.case_id = fall_nummer + try: + self.dt = datetime.strptime(datum_betreuung, "%Y-%m-%d %H:%M:%S") + except ValueError as e: + self.dt = datetime.strptime(datum_betreuung, "%Y-%m-%d %H:%M:%S") + self.duration_in_minutes = int(dauer_betreuung_in_min) + self.employee_nr = mitarbeiter_personalnummer + + self.employee = None + + def add_employee(self, employee): + self.employee = employee + + def add_care_to_case(lines, cases, employees): + """ + Adds the entries from TACS as instances of Care() to the respective Case(). + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: TACS_DATEN + ["patient_patientid", "patient_typ", "patient_status", "fall_nummer", "fall_typ", "fall_status", "datum_betreuung", "dauer_betreuung_in_min", ... + ["00013768220", "Standard Patient", "aktiv", "0006422111", "Standard Fall", "aktiv", "2018-03-11 00:00:00.0", "3", ... + ["00000552828", "Standard Patient", "aktiv", "0006454306", "Standard Fall", "aktiv", "2018-04-10 00:00:00.0", "20", ... + <-- continued --> + "mitarbeiter_personalnummer", "mitarbeiter_anstellungsnummer", "mitarbeiter_login", "BATCH_RUN_ID"] + "0301119", "00026556", "I0301119", "870"] + "0025908", "00014648", "I0025908", "870"] + + :param cases: Dictionary mapping case ids to Case() objects --> {"0003536421" : Case(), "0003473241" : Case(), ...} + :param employees: Dictionary mapping employee_ids to Employee() objects --> {'0032719' : Employee(), ... } + """ + logging.debug("add_care_to_case") + nr_case_not_found = 0 + nr_employee_created = 0 + nr_employee_found = 0 + for line in lines: + care = Care(*line) + + # discard if we don't have the case + case = cases.get(care.case_id, None) + if case is None: + nr_case_not_found += 1 + continue + case.add_care(care) + + # create employee if not already existing + employee = employees.get(care.employee_nr, None) + if employee is None: + employee = Employee(care.employee_nr) + nr_employee_created += 1 + else: + nr_employee_found += 1 + employees[employee.mitarbeiter_id] = employee + + care.add_employee(employee) + logging.info( + f"{nr_case_not_found} cases not found, {nr_employee_created} employees created, {nr_employee_found} employees found" + ) diff --git a/spitalhygiene/vre/src/main/python/vre/model/Case.py b/spitalhygiene/vre/src/main/python/vre/model/Case.py new file mode 100644 index 0000000..e93c8f3 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Case.py @@ -0,0 +1,208 @@ +from datetime import datetime +import logging + + +class Case: + def __init__( + self, + patient_id, + case_id, + case_typ, + case_status, + fal_ar, + beg_dt, + end_dt, + patient_typ, + patient_status, + ): + self.patient_id = patient_id + self.case_id = case_id + self.case_typ = case_typ + self.case_status = case_status + self.fal_ar = fal_ar + self.beg_dt = None + if beg_dt and beg_dt != "NULL": + self.beg_dt = datetime.strptime(beg_dt, "%Y-%m-%d") + self.end_dt = None + if end_dt and end_dt != "NULL": + self.end_dt = datetime.strptime(end_dt, "%Y-%m-%d") + self.patient_typ = patient_typ + self.patient_status = patient_status + self.appointments = [] + self.cares = [] + self.surgeries = [] + self.moves = dict() + self.moves_start = None + self.moves_end = None + self.referrers = set() + self.patient = None + self.medications = [] + self.icd_codes = [] + + def is_stationary(self): + """ + Stationary cases have "1" FALAR in SAP table NFAL + :return: + """ + return self.fal_ar == "1" + + def open_before_or_at_date(self, dt): + """ + Did the moves of this case start before or at dt? + :param dt: datetime.date + :return: + """ + if self.moves_start is None: + return False + else: + return self.moves_start.date() <= dt + + def closed_after_or_at_date(self, dt): + """ + Did the moves of this case end after or at dt? + :param dt: datetime.date + :return: + """ + if self.moves_end is None: + return False + else: + return self.moves_end.date() >= dt + + def add_referrer(self, partner): + """ + Add a business partner to the set of referrers for this case. + Referrers can be found in SAP IS-H table NFPZ, or in NBEW. + :param partner: + :return: + """ + self.referrers.add(partner) + + def add_surgery(self, surgery): + """ + Add a surgery to this case. + :param surgery: + :return: + """ + self.surgeries.append(surgery) + + def add_medication(self, medication): + """ + Add a medication to this case. + :param medication: Medication + :return: + """ + self.medications.append(medication) + + def add_appointment(self, appointment): + self.appointments.append(appointment) + + def add_care(self, care): + self.cares.append(care) + + def add_move(self, move): + self.moves[move.lfd_nr] = move + if move.bwe_dt is not None and ( (self.moves_end is None) or (move.bwe_dt > self.moves_end) ): + self.moves_end = move.bwe_dt + if move.bwi_dt is not None and ( (self.moves_start is None) or (move.bwi_dt < self.moves_start) ): + self.moves_start = move.bwi_dt + + def correct_move_enddt(self): + """ + This is required because we can't trust the end date of the movement data. + Call this only after all the movement data is loaded! + Helper function to fix missing movement end dates and times of this Fall: + Set the end date/time as the start date/time of the next move. Only use end date/time if there + is no next move. + """ + sorted_keys = sorted(self.moves) + for i, lfd_nr in enumerate(sorted_keys): + if i < (len(sorted_keys) - 1): + self.moves[lfd_nr].bwe_dt = self.moves[sorted_keys[i + 1]].bwi_dt + + def add_patient(self, p): + self.patient = p + + def add_icd_code(self, icd_code): + """ + Adds an ICD() code object to the self.icd_codes list. + + :param icd_code: an ICD() object + """ + self.icd_codes.append(icd_code) + + def get_length_of_stay(self): + """ + The length of stay is the duration between the start time of the first movement of the case and + the end time of the last movement. + :return: + """ + if self.fal_ar != "1": + return None + if self.moves_end is None: + return datetime.now() - self.moves_start + else: + return self.moves_end - self.moves_start + + def get_length_of_stay_until(self, dt): + """ + Timedelta between moves_start and moves_end or dt, whichever comes first. + :param dt: datetime.datetime + :return: datetime.timedelta + """ + if self.moves_end is None or (self.moves_end > dt): + return dt - self.moves_start + else: + return self.moves_end - self.moves_start + + def get_moves_before_dt(self, dt): + """ + The list of moves that start before a given datetime. + :param dt: datetime.datetime + :return: List of moves + """ + moves = [] + for move in self.moves.values(): + if move.bwi_dt < dt: + moves.append(move) + return moves + + @staticmethod + def create_case_map(lines, patienten): + """ + Read the case csv and create Case objects from the rows. Populate a dict with cases (case_id -> case) that are not 'storniert'. Note that the function goes both ways, i.e. it adds + Cases to Patients and vice versa. This function will be called by the HDFS_data_loader.patient_data() function. The lines argument corresponds to a csv.reader() instance + which supports the iterator protocol (see documentation for csv.reader in module "csv"). Each iteration over lines will contain a list of the following values + (EXCLUDING the header line): + >> TABLE NAME: V_LA_ISH_NFAL_NORM + [ "PATIENTID", "CASEID", "CASETYP", "CASESTATUS", "FALAR", "BEGDT", "ENDDT", "PATIENTTYP", "PATIENTSTATUS"] --> header line + [ "00008769940", "0003536421", "Standard Fall", "storniert", "2", "", "", "Standard Patient", "aktiv"] + [ "00008770123", "0003473241", "Standard Fall", "aktiv", "2", "", "2010-12-31", "Standard Patient", "aktiv"] + + :param patienten: Dictionary mapping patient ids to Patient() objects --> {"00001383264" : Patient(), "00001383310" : Patient(), ...} + + :return: Dictionary mapping case ids to Case() objects --> {"0003536421" : Case(), "0003473241" : Case(), ...} + """ + logging.debug("create_case_map") + nr_not_found = 0 + nr_ok = 0 + nr_not_stationary = 0 + cases = dict() + for line in lines: + fall = Case(*line) + if fall.fal_ar != '1': # exclude non-stationary cases (for stationary cases: Case().falar == '1' !) + nr_not_stationary += 1 + continue + if fall.case_status == "aktiv": # exclude entries where "CASESTATUS" is "storniert" + cases[fall.case_id] = fall + if patienten.get(fall.patient_id, None) is not None: + patienten[fall.patient_id].add_case(fall) + fall.add_patient(patienten[fall.patient_id]) + else: + nr_not_found += 1 + continue + else: + continue + nr_ok += 1 + + logging.info(f"{nr_ok} ok, {nr_not_found} patients not found, {nr_not_stationary} cases not stationary") + return cases diff --git a/spitalhygiene/vre/src/main/python/vre/model/Chop.py b/spitalhygiene/vre/src/main/python/vre/model/Chop.py new file mode 100644 index 0000000..1a1d44a --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Chop.py @@ -0,0 +1,112 @@ +import logging + +class Chop: + def __init__( + self, + chop_code, + chop_verwendungsjahr, + chop, + chop_code_level1, + chop_level1, + chop_code_level2, + chop_level2, + chop_code_level3, + chop_level3, + chop_code_level4, + chop_level4, + chop_code_level5, + chop_level5, + chop_code_level6, + chop_level6, + chop_status, + chop_sap_katalog_id, + ): + self.chop_code = chop_code + self.chop_verwendungsjahr = chop_verwendungsjahr + self.chop = chop + self.chop_code_level1 = chop_code_level1 + self.chop_level1 = chop_level1 + self.chop_code_level2 = chop_code_level2 + self.chop_level2 = chop_level2 + self.chop_code_level3 = chop_code_level3 + self.chop_level3 = chop_level3 + self.chop_code_level4 = chop_code_level4 + self.chop_level4 = chop_level4 + self.chop_code_level5 = chop_code_level5 + self.chop_level5 = chop_level5 + self.chop_code_level6 = chop_code_level6 + self.chop_level6 = chop_level6 + self.chop_status = chop_status + self.chop_sap_katalog_id = chop_sap_katalog_id + + self.cases = [] # Keep track of all cases that have this chop code + + def add_case(self, case): + """ + Adds a new case to this chop code. + """ + self.cases.append(case) + + def get_detailed_chop(self): + ''' + Get the description text from the highest available level for this code. + :return: + ''' + for field in [ + self.chop_level6, + self.chop_level5, + self.chop_level4, + self.chop_level3, + self.chop_level2, + self.chop_level1, + ]: + if field is not None and len(field) > 0 and field != "NULL": + return field + + def get_lowest_level_code(self): + ''' + The level 2 code represents the first number of the code. + E.g.: Z89.07.24 -> Z89 + :return: + ''' + return self.chop_code_level2 + + def create_chop_dict(lines): + """ + Creates and returns a dict of all chop codes. The key of a chop code is _ (different catalogs exist for different years). + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: V_DH_REF_CHOP + ["CHOPCODE", "CHOPVERWENDUNGSJAHR", "CHOP", "CHOPCODELEVEL1", "CHOPLEVEL1", "CHOPCODELEVEL2", ... + ["Z62.99.30", "2016", "Entnahme von Hoden- oder Neben...", "C11", "Operationen an den männlichen Geschlechtsorganen (60–64)", "Z62", ... + ["Z62.99.99", "2011", "Sonst. Operationen an den Ho", "C9", "Operationen am Verdauungstrakt (42–54)", "Z62", ... + <-- continued --> + "CHOPLEVEL2", "CHOPCODELEVEL3", "CHOPLEVEL3", "CHOPCODELEVEL4", "CHOPLEVEL4", "CHOPCODELEVEL5", "CHOPLEVEL5", ... + "Operationen an den Hoden", "Z62.9", "Sonstige Operationen an den Hoden", "Z62.99", "Sonstige Operationen an den Hoden", "Z62.99.0", "Detail der Subkategorie 62.99", ... + "Operationen an den Hoden", "Z62.9", "Sonstige Operationen an den Hoden", "Z62.99", "Sonstige Operationen an den Hoden", "Z62.99.99", "Sonstige Operationen an den Hoden, sonstige", ... + <-- continued --> + "CHOPCODELEVEL6", "CHOPLEVEL6", "CHOPSTATUS", "CHOPSAPKATALOGID"] + "Z62.99.30", "Entnahme von Hoden- oder Nebenhodengewebe zur Aufbereitung für die künstliche Insemination", "0", "16"] + "", "", "1", "10"] + + :return Dictionary mapping the chopcode_katalogid entries to Chop() obects --> { 'Z39.61.10_11': Chop(), ... } + """ + logging.debug("create_chop_dict") + chops = dict() + for line in lines: + chop = Chop(*line) + chops[chop.chop_code + "_" + chop.chop_sap_katalog_id] = chop # based on the schema in the docstring, this would yield "Z62.99.30_16" or "Z62.99.99_10" + logging.info(f"{len(chops)} chops created") + return chops + + def chop_code_stats(self, chops): + """ + Print frequency of different chop codes. + """ + for chop in chops.values(): + print( + str(len(chop.cases)) + + ": " + + chop.get_detailed_chop() + + " " + + chop.chop_code + ) diff --git a/spitalhygiene/vre/src/main/python/vre/model/Device.py b/spitalhygiene/vre/src/main/python/vre/model/Device.py new file mode 100644 index 0000000..0f0d714 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Device.py @@ -0,0 +1,60 @@ +import logging + +class Device: + ''' + Models a device from RAP. + Schema: GERAETID;GERAETNAME + ''' + def __init__(self, geraet_id, geraet_name): + self.geraet_id = geraet_id + self.geraet_name = geraet_name + + def create_device_map(lines): + ''' + Loads all devices into a dictionary based on lines in the csv file. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: V_DH_DIM_GERAET_CUR + ["GERAETID", "GERAETNAME"] + ["82250", "ANS-Fix"] + ["162101", "Waage"] + + Returns: A dictionary mapping device_ids to Device() object --> {'64174' : Device(), ... } + ''' + logging.debug("create_device_map") + devices = dict() + for line in lines: + device = Device(*line) + devices[device.geraet_id] = device + logging.info(f"{len(devices)} devices created") + return devices + + def add_device_to_appointment(lines, appointments, devices): + ''' + Adds the device in devices to the respective appointment in appointments based on lines in the csv file. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: V_DH_FACT_TERMINGERAET + ["TERMINID", "GERAETID", "TERMINSTART_TS", "TERMINENDE_TS", "DAUERINMIN"] + ["26266554", "123223", "2015-04-03 13:45:00.0000", "2015-04-03 15:45:00.0000", "120.000000"] + ["23678836", "38006", "2014-07-31 10:00:00.0000", "2014-07-31 10:30:00.0000", "30.000000"] + + :param appointments: Dictionary mapping appointment ids to Appointment() objects --> { '36830543' : Appointment(), ... } + :param devices: Dictionary mapping device_ids to Device() object --> {'64174' : Device(), ... } + ''' + logging.debug("add_device_to_appointment") + nr_appointment_not_found = 0 + nr_device_not_found = 0 + nr_ok = 0 + for line in lines: + appointment_id = line[0] + if appointments.get(appointment_id, None) is None: + nr_appointment_not_found += 1 + continue + device_id = line[1] + if devices.get(device_id, None) is None: + nr_device_not_found += 1 + continue + appointments[appointment_id].add_device(devices[device_id]) + nr_ok += 1 + logging.info( + f"{nr_ok} ok, {nr_appointment_not_found} appointments not found, {nr_device_not_found} devices not found" + ) diff --git a/spitalhygiene/vre/src/main/python/vre/model/Employee.py b/spitalhygiene/vre/src/main/python/vre/model/Employee.py new file mode 100644 index 0000000..18d5a10 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Employee.py @@ -0,0 +1,57 @@ +import logging + + +class Employee: + """ + Models an employee (doctor, nurse, etc) from RAP. + """ + + def __init__(self, mitarbeiter_id): + self.mitarbeiter_id = mitarbeiter_id + + def create_employee_map(lines): + """ + Read the appointment to employee file and create Employee() objects. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: V_DH_FACT_TERMINMITARBEITER + ["TERMINID", "MITARBEITERID", "TERMINSTART_TS", "TERMINENDE_TS", "DAUERINMIN"] + ["521664", "0063239", "2003-11-11 07:30:00.000", "2003-11-11 08:00:00.0000", "30.000000"] + ["521754", "X33671", "2003-11-10 09:15:00.000", "2003-11-10 09:45:00.0000", "30.000000"] + + Returns: Dictionary mapping employee_ids to Employee() objects --> {'0032719' : Employee(), ... } + """ + logging.debug("create_employee_map") + employee_dict = dict() + for line in lines: + employee = line[1] + employee_dict[employee] = Employee(employee) + logging.info(f"{len(employee_dict)} employees created") + return employee_dict + + def add_employee_to_appointment(lines, appointments, employees): + """ + Adds Employee() in employees to Appointment() in appointments based on the lines read from the csv file. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). + The underlying table is V_DH_FACT_TERMINMITARBEITER, and is identical to the one defined in the create_employee_map() method above. + + :param appointments: Dictionary mapping appointment ids to Appointment() objects --> { '36830543' : Appointment(), ... } + :param employees: Dictionary mapping employee_ids to Employee() objects --> {'0032719' : Employee(), ... } + """ + logging.debug("add_employee_to_appointment") + nr_employee_not_found = 0 + nr_appointment_not_found = 0 + nr_ok = 0 + for line in lines: + employee_id = line[1] + appointment_id = line[0] + if appointments.get(appointment_id, None) is None: + nr_appointment_not_found += 1 + continue + if employees.get(employee_id, None) is None: + nr_employee_not_found += 1 + continue + appointments[appointment_id].add_employee(employees[employee_id]) + nr_ok += 1 + logging.info( + f"{nr_ok} ok, {nr_appointment_not_found} appointments not found, {nr_employee_not_found} employees not found" + ) diff --git a/spitalhygiene/vre/src/main/python/vre/model/ICD.py b/spitalhygiene/vre/src/main/python/vre/model/ICD.py new file mode 100644 index 0000000..a908954 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/ICD.py @@ -0,0 +1,71 @@ +import datetime +import logging + +class ICD: + def __init__(self, falnr, dkey1, dkat, diadt, drg_cat): + self.fall_nummer = falnr + self.icd_code = dkey1 + self.catalogue_year = int(dkat) # catalogue year of the ICD code (2-digit integer) - may or may not be relevant + self.diagnosis_dt = datetime.datetime.strptime(diadt, '%Y-%m-%d') # date when ICD code was set + self.drg_category = drg_cat # single character specifying the DRG category + + self.cases = [] # List containing all cases with this particular ICD code (currently not used) + + def add_case(self, case): + """ + Adds a case to this particular ICD code (currently not used). + """ + self.cases.append(case) + + def create_icd_dict(lines): + """ + Creates and returns a dictionary of all icd codes. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: LA_ISH_NDIA + ['FALNR', 'DKEY1', 'DKAT1', 'DIADT', 'DRG_CATEGORY'] + ['0001832682', 'A41.9', '17', '2018-02-27', 'P'] + ['0001832682', 'R65.1', '17', '2018-02-27', 'S'] + + :return: Dictionary mapping the icd_code entries to ICD() objects --> { 'Z12.8': ICD(), ... } + """ + logging.debug("Creating ICD dictionary") + icd_dict = {} + + for each_line in lines: + this_icd = ICD(*each_line) + icd_dict[this_icd.icd_code] = this_icd + # Write success to log and return dictionary + logging.info(f'Successfully created {len(icd_dict.values())} ICD entries') + return icd_dict + + def add_icd_to_case(lines, cases): + """ + Adds ICD codes to cases based on the ICD.fall_nummer attribute. For details on how each line in the lines iterator object is formatted, see the function create_icd_dict() above. + + :param lines: Iterator object returning data directly from csv + :param cases: Dictionary mapping case ids to Case() objects --> {"0003536421" : Case(), "0003473241" : Case(), ...} + """ + logging.debug("Adding ICD codes to cases") + cases_not_found = 0 + cases_found = 0 + unique_case_ids = [] # list of unique case ids processed + + for each_line in lines: + this_icd = ICD(*each_line) + if cases.get(this_icd.fall_nummer) is not None: # default value for .get() is None + cases.get(this_icd.fall_nummer).add_icd_code(this_icd) + cases_found += 1 + unique_case_ids.append(cases.get(this_icd.fall_nummer)) + else: + cases_not_found += 1 + logging.info(f'Added {cases_found} ICD codes to {len(set(unique_case_ids))} relevant cases, {cases_not_found} cases not found') + + + + + + + + + + diff --git a/spitalhygiene/vre/src/main/python/vre/model/Medication.py b/spitalhygiene/vre/src/main/python/vre/model/Medication.py new file mode 100644 index 0000000..7cdc298 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Medication.py @@ -0,0 +1,76 @@ +from datetime import datetime +import logging + + +class Medication: + def __init__( + self, + patient_id, + case_id, + drug_text, + drug_atc, + drug_quantity, + drug_unit, + drug_dispform, + drug_submission, + ): + self.patient_id = patient_id + self.case_id = case_id + self.drug_text = drug_text + self.drug_atc = drug_atc + self.drug_quantity = drug_quantity + self.drug_unit = drug_unit + self.drug_dispform = drug_dispform + self.drug_submission = datetime.strptime( + drug_submission, "%Y-%m-%d %H:%M:%S" + ) + + def is_antibiotic(self): + """ + Does the ATC code start with J01? + :return: boolean + """ + return self.drug_atc.startswith("J01") + + def create_drug_map(lines): + """ + Creates a dictionary of ATC codes to human readable drug names based on the entries in the CSV file. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: V_LA_IPD_DRUG_NORM + ["PATIENTID", "CASEID", "DRUG_TEXT", "DRUG_ATC", "DRUG_QUANTITY", "DRUG_UNIT", "DRUG_DISPFORM", "DRUG_SUBMISSION"] + ["00001711342", "0006437617", "Torem Tabl 10 mg (Torasemid)", "C03CA04", "2.0000000000000", "Stk", "p.o.", "2018-03-24 09:52:28.0000000"] + ["00001711342", "0006437617", "Ecofenac Sandoz Lipogel 1 % (Diclofenac)", "M02AA15", "1.0000000000000", "Dos", "lokal / topisch", "2018-03-24 09:52:28.0000000"] + + Returns: A dictionary mapping drug codes to their respective text description --> {'B02BA01' : 'NaCl Braun Inf Lös 0.9 % 500 ml (Natriumchlorid)', ... } + """ + logging.debug("create_drug_map") + drugs = dict() + for line in lines: + if drugs.get(line[3], None) is None: + drugs[line[3]] = line[2] + logging.info(f"{len(drugs)} drugs created") + return drugs + + def add_medication_to_case(lines, cases): + """ + Read all the medication entries from the V_LA_IPD_DRUG_NORM table, and adds the drug (Medication() object) to the corresponding Case() (in cases) via Case.add_medication(). + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). + For a schema of the V_LA_IPD_DRUG_NORM table, see the function create_drug_map() above. + + :param cases: Dictionary mapping case ids to Case() --> {'0005976205' : Case(), ... } + """ + logging.debug("add_medication_to_case") + nr_cases_not_found = 0 + nr_malformed = 0 + nr_ok = 0 + for line in lines: + if len(line) != 8: + nr_malformed += 1 + continue + medication = Medication(*line) + if cases.get(medication.case_id, None) is None: + nr_cases_not_found += 1 + continue + cases.get(medication.case_id).add_medication(medication) + nr_ok += 1 + logging.info(f"{nr_ok} ok, {nr_cases_not_found} cases not found, {nr_malformed} malformed") diff --git a/spitalhygiene/vre/src/main/python/vre/model/Move.py b/spitalhygiene/vre/src/main/python/vre/model/Move.py new file mode 100644 index 0000000..0574c5d --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Move.py @@ -0,0 +1,154 @@ +from Room import Room +from Ward import Ward +from datetime import datetime +import logging + + +class Move: + def __init__( + self, + fal_nr, + lfd_nr, + bew_ty, + bw_art, + bwi_dt, + bwi_zt, + statu, + bwe_dt, + bwe_zt, + lfd_ref, + kz_txt, + org_fa, + org_pf, + org_au, + zimmr, + bett, + storn, + ext_kh + ): + self.fal_nr = fal_nr + self.lfd_nr = int(lfd_nr) + self.bew_ty = bew_ty + self.bw_art = bw_art + self.bwi_dt = datetime.strptime( + bwi_dt + " " + bwi_zt, "%Y-%m-%d %H:%M:%S" + ) + self.statu = statu + self.bwe_dt = None + try: + if bwe_zt == '24:00:00.000000000': bwe_zt = "23:59:59.000000000" + self.bwe_dt = datetime.strptime( + bwe_dt + " " + bwe_zt, "%Y-%m-%d %H:%M:%S.000000000" + ) + except ValueError: + pass + self.ldf_ref = lfd_ref + self.kz_txt = kz_txt + self.org_fa = org_fa + self.org_pf = org_pf + self.org_au = org_au + self.zimmr = zimmr + self.bett = bett + self.storn = storn + self.ext_kh = ext_kh + self.room = None + self.ward = None + self.case = None + + def add_room(self, r): + self.room = r + + def add_ward(self, ward): + self.ward = ward + + def add_case(self, c): + self.case = c + + def get_duration(self): + end_dt = self.bwe_dt if self.bwe_dt is not None else datetime.now() + return end_dt - self.bwi_dt + + def create_bwart_map(lines): + bwart = dict() + for line in lines: + bwart[line[0]] = line[1] + return bwart + + def add_move_to_case(lines, faelle, rooms, wards, partners): + """ + Reads the moves csv and performs the following: + --> creates a Move() object from the read-in line data + --> Adds the created Move() to the corresponding Case() + --> Extracts the Room() from each Move() and adds them "to each other" + --> Extracts the Ward() from each Move() and adds them "to each other" + --> Adds referring hospital to the Case() and vice versa + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The table from which entries are read is structured as follows: + >> TABLE NAME: LA_ISH_NBEW + ["FALNR", "LFDNR", "BEWTY", "BWART", "BWIDT", "BWIZT", "STATU", "BWEDT", "BWEZT", "LFDREF", "KZTXT", "ORGFA", "ORGPF", "ORGAU", "ZIMMR", "BETT", "STORN", "EXTKH"] + ["0004496041", "3", "4", "SB", "2014-01-17", "16:15:00", "30", "2014-01-17", "16:15:00.000", "0", "", "DIAA", "DIAA", "", "", "", "", ""] + ["0004496042", "1", "4", "BE", "2014-03-10", "08:15:00", "30", "2014-03-10", "08:15:00.000", "0", "ej/ nü CT um 10.30 h", "ENDA", "ENDA", "IICA", "", "", "", ""] + + :param faelle: Dictionary mapping case ids to Case() --> {'0005976205' : Case(), ... } + :param rooms: Dictionary mapping room names to Room() --> {'BH N 125' : Room(), ... } + :param room_ids: Dictionary mapping room IDs to Room() --> {'127803' : Room(), ... } + :param wards: Dictionary mapping ward names to Ward() --> {'N NORD' : Ward(), ... } + :param partners: Dictionary mapping partner ids to Partner() --> {'0010000990' : Partner(), ... } + """ + logging.debug("add_move_to_case") + nr_not_found = 0 + nr_not_formatted = 0 + nr_ok = 0 + nr_wards_updated = 0 + for counter, line in enumerate(lines): + if len(line) != 18: + nr_not_formatted += 1 + else: + move = Move(*line) + # don't consider cancelled movements + if move.storn == "X": + continue + + if faelle.get(move.fal_nr, None) is not None: + faelle[move.fal_nr].add_move(move) + move.add_case(faelle[move.fal_nr]) + else: + nr_not_found += 1 + continue + ward = None + ### Add ward to move and vice versa + if move.org_pf != "" and move.org_pf != "NULL": + if wards.get(move.org_pf, None) is None: + ward = Ward(move.org_pf) + wards[move.org_pf] = ward + wards[move.org_pf].add_move(move) + move.add_ward(wards[move.org_pf]) + ### Add move to room and vice versa (including an update of the Room().ward attribute) + if move.zimmr != "" and move.zimmr != "NULL": + if rooms.get(move.zimmr, None) is None: + # Note that the rooms are primarily identified through their name + # The names in this file come from SAP (without an associated ID), so they will NOT match the names already present in the rooms dictionary ! + this_room = Room(move.zimmr) + rooms[move.zimmr] = this_room + + # In order to extract the Room ID, we need to 'backtrace' the key in room_ids for which room_ids[key] == move.zimmr (this will not be available for most Rooms) + # If a backtrace is not possible, the room object will be initiated without an ID + # correct_room_id = [(value_tuple[0], value_tuple[1].name) for value_tuple in room_ids.items() if value_tuple[1] == move.zimmr] + # if len(correct_room_id) == 1: + # logging.info(f"Found room ID for room name {move.zimmr} !") + # r = Room(move.zimmr, correct_room_id[0][0] ) # correct_room_id at this point will be a list containing one tuple --> [ ('123456', 'BH O 128') ] + # else: + # r = Room(move.zimmr) # Create the Room() object without providing an ID + # rooms[move.zimmr] = r + # Then add the ward to this room, and update moves with rooms and vice versa + rooms[move.zimmr].add_ward(ward) + rooms[move.zimmr].add_move(move) + move.add_room(rooms[move.zimmr]) + nr_wards_updated += 1 + ### Parse patients from external referrers + if move.ext_kh != "": + if move.case is not None and partners.get(move.ext_kh, None) is not None: + partners[move.ext_kh].add_case(move.case) + move.case.add_referrer(partners[move.ext_kh]) + nr_ok += 1 + + logging.info(f"{nr_ok} ok, {nr_not_found} cases not found, {nr_not_formatted} malformed, {nr_wards_updated} wards updated") diff --git a/spitalhygiene/vre/src/main/python/vre/model/Partner.py b/spitalhygiene/vre/src/main/python/vre/model/Partner.py new file mode 100644 index 0000000..e629876 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Partner.py @@ -0,0 +1,97 @@ +import logging + +class Partner: + ''' + Defines a Business partner as stored in SAP IS-H table NGPA. + This is used to track referring physicians for the patients' cases. + The cases that were referred by a physician are tracked in referred_cases. + ''' + def __init__(self, gp_art, name1, name2, name3, land, pstlz, ort, ort2, stras, krkhs): + ''' + Constructor. Subset of fields from IS-H table NGPA. + :param gp_art: + :param name1: + :param name2: + :param name3: + :param land: + :param pstlz: + :param ort: + :param ort2: + :param stras: + :param krkhs: + ''' + self.gp_art = gp_art + self.name1 = name1 + self.name2 = name2 + self.name3 = name3 + self.land = land + self.pstlz = pstlz + self.ort = ort + self.ort2 = ort2 + self.stras = stras + self.krkhs = krkhs + + self.referred_cases = dict() + + def add_case(self, case): + ''' + Track the cases that a physician has referred. + :param case: + :return: + ''' + self.referred_cases[case.case_id] = case + + @staticmethod + def create_partner_map(lines): + ''' + Creates and returns a map of business partners from a csv reader. This function will be called by the HDFS_data_loader.patient_data() function. The lines argument corresponds to a + csv.reader() instance which supports the iterator protocol (see documentation for csv.reader in module "csv"). Each iteration over lines will contain a list of the + following values (EXCLUDING the header line): + >> TABLE NAME: LA_ISH_NGPA + [ "GPART", "NAME1", "NAME2", "NAME3", "LAND", "PSTLZ", "ORT", "ORT2", "STRAS", "KRKHS"] --> header line + [ "1001503842", "Fontanellaz", "Christian David", "", "CH", "3010", "Bern", "", "", ""] + [ "1001503845", "Dulcey", "Andrea Sara", "", "CH", "3010", "Bern", "", "", ""] + + Returns: Dictionary mapping partners to Partner() objects --> {'1001503842' : Partner(), '1001503845' : Partner(), ... } + ''' + nr_malformed = 0 + partners = dict() + for line in lines: + if len(line) != 10: + nr_malformed += 1 + continue + partner = Partner(*line) + partners[partner.gp_art] = partner + + logging.info(f"{len(partners)} created, {nr_malformed} partners malformed") + return partners + + @staticmethod + def add_partners_to_cases(lines, cases, partners): + ''' + Reads lines from csv reader originating from SAP IS-H table NFPZ, and updates the referring physician (Partner() object) from partners to the corresponding case, + and also adds the corresponding Case() to Partner() from cases. This function is called by the HDFS_data_loader.patient_data() method. + The table used for updating cases has the following structure: + >> TABLE NAME: LA_ISH_NFPZ + [ "EARZT", "FARZT", "FALNR", "LFDNR", "PERNR", "STORN"] + [ "H", "2", "0006451992" "1", "0010217016" ""] + [ "H", "2", "0006451992", "3", "0010217016", ""] + + Referring physicians (EARZT = 'U') are added only to cases which are NOT cancelled, i.e. STORN != 'X'. + ''' + logging.debug("add_partners_to_cases") + nr_cases_not_found = 0 + nr_partners_not_found = 0 + nr_ok = 0 + for line in lines: + if line[0] == 'U' and line[5] != 'X': # line[5] corresponds to the "STORN" column ('X' --> storniert) + if cases.get(line[2], None) is None: + nr_cases_not_found += 1 + continue + if partners.get(line[4], None) is None: + nr_partners_not_found += 1 + continue + cases[line[2]].add_referrer(partners[line[4]]) + partners[line[4]].add_case(cases[line[2]]) + nr_ok += 1 + logging.info(f"{nr_ok} ok, {nr_cases_not_found} cases not found, {nr_partners_not_found} partners not found") diff --git a/spitalhygiene/vre/src/main/python/vre/model/Patient.py b/spitalhygiene/vre/src/main/python/vre/model/Patient.py new file mode 100644 index 0000000..2ee1af2 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Patient.py @@ -0,0 +1,464 @@ +import datetime +import logging +from dateutil.relativedelta import relativedelta + + +class Patient: + def __init__(self, patient_id, geschlecht, geburtsdatum, plz, wohnort, kanton, sprache): + self.patient_id = patient_id + self.geschlecht = geschlecht + self.geburtsdatum = None + if geburtsdatum: + self.geburtsdatum = datetime.datetime.strptime(geburtsdatum, "%Y-%m-%d") + self.plz = plz + self.wohnort = wohnort + self.kanton = kanton + self.sprache = sprache + self.cases = dict() + self.risks = dict() # dictionary mapping dt.dt() objects to Risk() objects, indicating at which datetime a particular VRE code has been entered in one of the Insel systems + + def get_relevant_case_and_date(self): + case = self.get_relevant_case() + if case is None: + return (None, None) + relevant_date = self.get_relevant_date() + dt = datetime.datetime.combine( + relevant_date, datetime.datetime.min.time() + ) # need datetime, not date + return (case, dt) + + def add_case(self, case): + self.cases[case.case_id] = case + + def add_risk(self, risk): + """ + Adds a new risk to the Patients risk dict. + :param risk: + :return: + """ + self.risks[risk.entry_date] = risk + + def has_risk(self, code_text_list=[(32, None), (42, None), (142, None)]): + """ + Returns true if there if at least one of the risk_code, risk_text tuples are found in the Patient's risk dict. + risk_text can be none if the text does not matter. False if none of the risks is found. + :param code_text_list: + :return: + """ + for code_text in code_text_list: + if self.risks.get(code_text[0], None) is not None: + if ( + code_text[1] is None + or self.risks[code_text[0]].kz_txt == code_text[1] + ): + return True + return False + + def get_risk_date(self, code_text_list=[(32, None), (42, None), (142, None)]): + """ + Identify risk date for a patient. + + :param code_text_list: currently a list of the form --> [(32, None), (42, None), (142, None)] (default value) + + :return: Date of the first risk from the code_text_list that is found in the Patient's risk dict, in the form of a datetime.datetime() object + If none of the risks is found, None is returned instead. + """ + for code_text in code_text_list: + if self.risks.get(code_text[0], None) is not None: + if code_text[1] is None or self.risks[code_text[0]].kz_txt == code_text[1]: + return self.risks[code_text[0]].er_dt + return None + + def get_age(self): + """ + Calculates age at relevant date, based on birth date. + None if no birth date is in the data or no relevant date + :return: + """ + dt = self.get_relevant_date() + if dt is None: + return None + if self.geburtsdatum is None: + return None + + return ( + dt.year + - self.geburtsdatum.year + - ((dt.month, dt.day) < (self.geburtsdatum.month, self.geburtsdatum.day)) + ) + + def get_relevant_date(self, dt=datetime.datetime.now().date()): + """ + Definition of relevant date: + For patients with risk factor: The date attached to the risk factor. + For patients without risk factor: Date as provided in dt, default date = now. + :return: + """ + risk_dt = self.get_risk_date() + if risk_dt is not None: + dt = risk_dt.date() + return dt + + def get_relevant_case(self, dt=datetime.datetime.now().date(), since=datetime.datetime(2017, 12, 31, 0, 0).date() ): + """ + Definition of relevant case: + The most recent stationary case, which was still open during relevant date or closed after "since" date. + :param dt: Relevant date for patients without risk factor. + :param since: Relevant case must still be open at "since" + :return: A Case() object in case there is a relevant case, or None otherwise + """ + relevant_dt = self.get_relevant_date(dt) + + # candidate relevant case must be + # 1. stationary + # 2. open before "relevant_dt" + # 3. closed after "since" + # from all candidates we take the one with highest closing date + + relevant_case = None + for case in self.cases.values(): + if ( + case.is_stationary() + and case.open_before_or_at_date(relevant_dt) + and case.closed_after_or_at_date(since) + ): + if relevant_case is None or case.closed_after_or_at_date( relevant_case.moves_end.date() ): + # Here we make sure to consider only the LATEST case, by comparing whether case() was closed after the closing date of the relevant case --> update relevant case ! + relevant_case = case + return relevant_case + + def get_referrers(self): + """ + Find referrers for relevant case. + :return: + """ + case = self.get_relevant_case() + return case.referrers + + def get_length_of_relevant_case(self): + """ + For the relevant case, length of stay is defined as the period between the moves_start and moves_end, + or between moves_start and relevant date if the case is still open at relevant date. + For non-stationary cases, length of stay is not defined (None). + :param dt: datetime.datetime + :return: datetime.timedelta, None if no relevant case + """ + (case, dt) = self.get_relevant_case_and_date() + if case is None: + return None + return case.get_length_of_stay_until(dt) + + def get_relevant_rooms(self): + """ + Set of names of rooms visited during relevant case before relevant date. + Rooms visits can come from SAP IS-H or from RAP. + :return: set of room names, None if no relevant case + """ + (case, dt) = self.get_relevant_case_and_date() + if case is None: + return None + rooms = set() + # moves from SAP IS-H + moves = case.get_moves_before_dt(dt) + for move in moves: + if move.room is not None: + rooms.add(move.room.name) + # appointments from RAP + for appointment in case.appointments: + if appointment.termin_datum < dt: + for room in appointment.rooms: + rooms.add(room.name) + return rooms + + def has_icu_stay(self, orgs): + """ + The patient has a stay in ICU during relevant case before relevant date if there is a move to one of the organizational units provided in orgs. + :param orgs: list of ICU organizational unit names + :return: boolean, False if no relevant case + """ + (case, dt) = self.get_relevant_case_and_date() + if case is None: + return False + moves = case.get_moves_before_dt(dt) + for move in moves: + if move.org_pf in orgs: + return True + return False + + def get_nr_cases(self, delta=relativedelta(years=1)): + """ + How many SAP cases (stationary and ambulatory) did the patient have in one year (or delta provided) before the relevant date. + (moves_start is before relevant_date and moves_end after relevant_date - delta + :return: int + """ + nr_cases = 0 + relevant_date = self.get_relevant_date() + dt = datetime.datetime.combine( + relevant_date, datetime.datetime.min.time() + ) # need datetime, not date + for case in self.cases.values(): + if case.moves_start is not None and case.moves_end is not None: + if case.moves_start <= dt and case.moves_end >= (dt - delta): + nr_cases += 1 + return nr_cases + + def get_antibiotic_exposure(self): + """ + Which antibiotics did the patient get, during which days in the relevant case, before the relevant date. + :return: dict from ATC code to set of date, None if no relevant case + """ + (case, dt) = self.get_relevant_case_and_date() + if case is None: + return None + relevant_medications = dict() + for medication in case.medications: + if medication.is_antibiotic() and ( + case.moves_start <= medication.drug_submission <= dt + ): + if relevant_medications.get(medication.drug_atc, None) is None: + date_set = set() + date_set.add(medication.drug_submission.date()) + relevant_medications[medication.drug_atc] = date_set + else: + relevant_medications[medication.drug_atc].add( + medication.drug_submission.date() + ) + + return relevant_medications + + def get_chop_codes(self): + """ + Which chop codes was the patient exposed to during the relevant case, before the relevant date. + :return: Set of Chops, None if no relevant case + """ + (case, dt) = self.get_relevant_case_and_date() + if case is None: + return None + relevant_chops = set() + for surgery in case.surgeries: + if surgery.bgd_op <= dt: + relevant_chops.add(surgery.chop) + return relevant_chops + + def has_surgery(self): + """ + Does the patient have a chop code during relevant case before relevant date? + :return: boolean + """ + relevant_chops = self.get_chop_codes() + return False if relevant_chops is None else len(relevant_chops) > 0 + + def get_dispform(self): + """ + What kind of forms of drug administration happened during the relevant case before the relevant date? + :return: set of names of administration forms, None if no relevant case + """ + (case, dt) = self.get_relevant_case_and_date() + if case is None: + return None + dispforms = set() + for medication in case.medications: + if case.moves_start <= medication.drug_submission <= dt: + dispforms.add(medication.drug_dispform) + return dispforms + + def get_employees(self): + """ + employee IDS and duration of care or appointment of employees that were involved in appointments or care + during the relevant case, before the relevant date. + :return: set of employee_id + """ + (case, dt) = self.get_relevant_case_and_date() + if case is None: + return None + employees = dict() + for appointment in case.appointments: + if appointment.termin_datum < dt: + for employee in appointment.employees: + employees[employee.mitarbeiter_id] = ( + appointment.dauer_in_min + if employees.get(employee.mitarbeiter_id, None) is None + else employees[employee.mitarbeiter_id] + appointment.dauer_in_min + ) + for care in case.cares: + if care.dt < dt: + employees[care.employee.mitarbeiter_id] = ( + care.duration_in_minutes + if employees.get(care.employee.mitarbeiter_id, None) is None + else employees[care.employee.mitarbeiter_id] + care.duration_in_minutes + ) + return employees + + def get_devices(self): + """ + Names of devices that were used during the relevant case, before the relevant date. + :return: set of device names, None if no relevant case + """ + (case, dt) = self.get_relevant_case_and_date() + if case is None: + return None + device_names = set() + for appointment in case.appointments: + if appointment.termin_datum < dt: + for device in appointment.devices: + device_names.add(device.geraet_name) + return device_names + + def get_partner(self): + """ + Returns the referring partner(s) for the relevant case. + :return: set of Partner + """ + (case, dt) = self.get_relevant_case_and_date() + if case is None: + return None + return case.referrers + + def get_label(self): + """ + All patients are categorized with the following labels: + -1: no relevant case + 0: unknown + 1: screening with unconclusive results + 2: screening negative + 3: screening positive + Higher number labels take precedence over lower ones. + :return: int + """ + label = -1 + if self.get_relevant_case() is not None: + label = 0 + if self.has_risk([(42, None)]): + label = 1 + if self.has_risk([(142, None)]): + label = 2 + if self.has_risk([(32, None)]): + label = 3 + return label + + def get_features(self): + """ + Creates the sparse feature vector for this patient. + + :return: Dictionary mapping feature names to the values for this patient --> {"length_of_stay" : 47, "nr_cases" : 3, ... } + If the patient has no relevant case, None is returned instead. + """ + features = dict() + if self.get_relevant_case() is None: + return None + + length_of_stay = self.get_length_of_relevant_case() + features["length_of_stay"] = length_of_stay.days + + features["nr_cases"] = self.get_nr_cases() + + features["age"] = self.get_age() + features["gender"] = self.geschlecht + features["language"] = self.sprache + features["plz"] = self.plz + features["kanton"] = self.kanton + + features["surgery"] = self.has_surgery() + + features["icu"] = self.has_icu_stay( # True if the patient has spent time in one of the ICU units, False otherwise + [ + "IN E GR", + "INEBL 1", + "INEBL 2", + "INEGE 1", + "INEGE 2", + "E108-09", + "E116", + "E113-15", + "E120-21", + ] + ) + + antibiotic_exposure = self.get_antibiotic_exposure() + for antibiotic_atc, days_administred in antibiotic_exposure.items(): + features["antibiotic=" + antibiotic_atc] = len(days_administred) + + dispforms = self.get_dispform() + if dispforms is not None: + for dispform in dispforms: + features["dispform=" + dispform] = True + + chops = self.get_chop_codes() + if chops is not None: + for chop in chops: + features["chop=" + chop.get_lowest_level_code()] = True + + stat_rooms = self.get_relevant_rooms() + for room in stat_rooms: + features["room=" + room] = True + + stat_employees = self.get_employees() + for employee_id, employee_duration in stat_employees.items(): + features["employee=" + employee_id] = employee_duration + + stat_devices = self.get_devices() + for device in stat_devices: + features["device=" + device] = True + + # Extract all ICD codes from the relevant case ONLY + relevant_case = self.get_relevant_case() # returns a Case() object corresponding to the relevant case for this patient + for icd_code in relevant_case.icd_codes: # Case.idc_codes is a list of ICD() objects for this particular case + features['icd=' + icd_code.icd_code] = True + + return features + + def get_location_info(self, focus_date, comparison_type='exact'): + """Returns ward, room and bed information for a patient at a specific date. + + This function will go through all Move() objects of each Cases() object of this patient, and return a tuple of + Move() objects for which + + Move().bwi_dt <= focus_date <= Move().bwe_dt + + The exact location of a patient at focus_date can then be extracted from the ``Move().org_fa``, + ``Move().org_pf``, ``Move().org_au``, ``Move().zimmr`` and ``Move().bett`` attributes. + + Args: + focus_date (datetime.date()): Date for which all moves are to be extracted from a patient + comparison_type (str): Type of comparison between Move() objects and focus_date. If set to + ``exact`` (the default), only Move() objects with non-None Move().bwi_dt + `and` Move().bwe_dt attributes will be considered. + + Returns: + tuple: tuple of Move() objects for which Move().bwi_dt < focus_date < Move().bwe_dt + """ + location_moves = [] + + for each_case in self.cases.values(): + for each_move in each_case.moves.values(): + if comparison_type == 'exact' and (each_move.bwi_dt is None or each_move.bwe_dt is None): + continue + # bwi_dt_date = datetime.date(each_move.bwi_dt.day, each_move.bwi_dt.month, each_move.bwi_dt.day) + # bwe_dt_date = datetime.date(each_move.bwe_dt.day, each_move.bwe_dt.month, each_move.bwe_dt.day) + if each_move.bwi_dt.date() <= focus_date <= each_move.bwe_dt.date(): + location_moves.append(each_move) + + return tuple(location_moves) + + @staticmethod + def create_patient_dict(lines): + """ + Read the patient csv and create Patient objects from the rows. + Populate a dict (patient_id -> patient). This function will be called by the HDFS_data_loader.patient_data() function. The lines argument corresponds to a csv.reader() instance + which supports the iterator protocol (see documentation for csv.reader in module "csv"). Each iteration over lines will contain a list of the following values + (EXCLUDING the header line): + >> TABLE NAME: V_DH_DIM_PATIENT_CUR + [ "PATIENTID" , "GESCHLECHT" , "GEBURTSDATUM" , "PLZ" , "WOHNORT" , "KANTON" , "SPRACHE"] --> header line (for documentation purposes only) + [ "00001383264" , "weiblich" , "1965-03-15" , "3072" , "Ostermundigen" , "BE" , "Deutsch"] + [ "00001383310" , "weiblich" , "1949-02-11" , "3006" , "Bern" , "BE" , "Russisch"] + + Returns: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} + """ + logging.debug("create_patient_dict") + patients = dict() + for line in lines: + patient = Patient(*line) + patients[patient.patient_id] = patient + + logging.info(f"{len(patients)} patients created") + return patients diff --git a/spitalhygiene/vre/src/main/python/vre/model/Risk.py b/spitalhygiene/vre/src/main/python/vre/model/Risk.py new file mode 100644 index 0000000..f219668 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Risk.py @@ -0,0 +1,184 @@ +from datetime import datetime +import logging + + +class Risk: + def __init__(self, auftrag_nbr, erfassung, entnahme, pat_nr, sex, PID, fall_nr, auftraggeber, kostenstelle, + material_type, transport, resultat, vreih, analyse_methode, screening_context, ward, options): + """Initiates a Risk (i.e. Screening) object. + - Auftrag_Nummer + - Erfassung + - Entnahme + - Patient_Number + - Geschlecht + - PID + - Fall_Nummer + - Auftraggeber + - Kostenstelle + - Material_Typ + - Transport + - Resultat + - vreih + - Analyse_Methode + - Screening_Context + - Ward + - Options + :param patient_id: + :param screen_code: + :param entry_date: + :param context: + """ + self.auftrag_nbr = auftrag_nbr + self.erfassung = datetime.strptime(erfassung, '%d.%m.%Y').date() if erfassung != '' else None + self.entnahme = datetime.strptime(entnahme, '%d.%m.%Y').date() if entnahme != '' else None + self.patient_nbr = pat_nr + self.patient_sex = sex + # Important note: adjust PID to have 11 digits, as it corresponds to [Patient_Number] + [Prüfziffer] + # --> used for matching to patient IDs in the VRE model ! + self.pid = ''.join(['0' for i in range(0, 11-len(PID))] + [PID]) + self.fall_nbr = fall_nr + self.auftraggeber = auftraggeber + self.kostenstelle = kostenstelle + self.material_type = material_type + self.transport = transport + self.result = resultat + self.vreih = vreih + self.analysis_method = analyse_methode + self.screening_context = screening_context + self.ward = ward + self.options = options + + def old_init(self, patient_id, screen_code, entry_date, context): + print('hi') # old __init function + + def NOLONGERUSED_add_risk_to_patient(lines, patients): + """Reads the risk csv, create Risk objects from the rows and adds these to the respective patients. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: V_LA_ISH_NRSF_NORM + ["PATIENTID", "RSFNR", "KZTXT", "ERDAT", "ERTIM"] + ["00004887743", "000042", "Screening auf VRE. Spezielle L", "2018-08-23", "10:25:30"] + ["00004963016", "000042", "Screening auf VRE. Spezielle L", "2018-05-09", "15:48:48"] + + :param patients: Dictionary mapping patient ids to Patient() --> {'00008301433' : Patient(), ... } + """ + logging.debug("add_risk_to_patient") + nr_not_found = 0 + nr_ok = 0 + for line in lines: + risk = Risk(*line) + if patients.get(risk.patient_id, None) is not None: + patients[risk.patient_id].add_risk(risk) + else: + nr_not_found += 1 + continue + nr_ok += 1 + logging.info(f"{nr_ok} risks added, {nr_not_found} patients not found for risk") + + def NOLONGERUSED_add_deleted_risk_to_patient(lines, patients): + ''' + Read the deleted risk csv and creates Risk objects with code '000142' for deleted VRE screening from the rows. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + + >> TABLE NAME: deleted_screenings + ["VNAME", "NNAME", "PATIENTID", "GBDAT", "ScreeningDate"] + ["Margarete", "Bucher", "00014616742", "1963-11-11", "2018-03-12"] + ["Edouard", "Kurth", "00014533820", "1954-02-15", "2018-02-16"] + + Discard entries which are missing the date of screening. + + :param patients: Dictionary mapping patient ids to Patient() --> {'00008301433' : Patient(), ... } + ''' + logging.debug("add_deleted_risk_to_patient") + nr_not_found = 0 + nr_malformed = 0 + nr_ok = 0 + for line in lines: + if len(line[4]) > 0 and line[4] != "NULL": + deleted_risk = Risk(*[line[2], '000142', 'Deleted VRE Screening', line[4], '00:00:00']) + else: + nr_malformed += 1 + continue + if patients.get(deleted_risk.patient_id, None) is not None: + patients[deleted_risk.patient_id].add_risk(deleted_risk) + else: + nr_not_found += 1 + continue + nr_ok += 1 + logging.info(f"{nr_ok} risks added, {nr_not_found} patients not found for deleted risk, {nr_malformed} malformed.") + + @staticmethod + def add_screening_data_to_patients(lines, patient_dict): + """Adds screening data to all patients in the model. + + Reads the custom-made VRE_Screenings_Final.csv file, which is produced in the Python Project "VRE Aggregation". + This file is created from manually extracted IFIK data, and cannot be currently extracted automatically on + Hadoop. This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator + object). The underlying file is the ONLY necessary file for VRE results, and contains an aggregation of + all IFIK screening results. Note that this file also contains an `Options` column which is used as a basis for + validating ward data, since a particular screening type may be active in multiple wards simultaneously. As a + consequence, the `Move()` data associated with all patients in the model must be used to correctly assign the + ward in which a particular screening has taken place. The raw input file contains the following columns: + + - Auftrag_Nummer + - Erfassung + - Entnahme + - Patient_Number + - Geschlecht + - PID + - Fall_Nummer + - Auftraggeber + - Kostenstelle + - Material_Typ + - Transport + - Resultat + - vreih + - Analyse_Methode + - Screening_Context + - Ward + - Options + + More details on the exact structure of the read-in file can be found in the Python Project "VRE Aggregation". + + Note that all screening results are converted into a Risk() object, validated, and added to Patient() objects + in patient_dict using the ``Patient().add_risk()`` function. + + Args: + lines (iterator): iterator object of the to-be-read file `not` containing the header line + patient_dict (dict): Dictionary mapping patient ids to Patient() --> {'00008301433' : Patient(), ... } + """ + move_wards = [] + screening_wards = [] + logging.debug("adding_all_screenings_to_patients") + nr_pat_not_found = 0 + nr_ok = 0 + for line in lines: + this_risk = Risk(*line) + if patient_dict.get(this_risk.pid) is None: # Check whether or not PID exists + nr_pat_not_found += 1 + continue + potential_moves = patient_dict.get(this_risk.pid).get_location_info(focus_date=this_risk.erfassung) + if len(potential_moves) > 0: # indicates at least one potential match + move_wards.append('+'.join([each_move.org_fa for each_move in potential_moves])) + screening_wards.append(this_risk.options) + nr_ok += 1 + # print results to file + with open('match_results.txt', 'w') as writefile: + for i in range(len(move_wards)): + writefile.write(f"{move_wards[i]}; {screening_wards[i]}\n") + + + + + + # patient_dict[this_risk.patient_id].add_risk(this_risk) + # nr_ok += 1 + # else: + # nr_pat_not_found += 1 + # continue + logging.info(f"{nr_ok} screenings added, {nr_pat_not_found} patients from VRE screening data not found.") + + + + + + diff --git a/spitalhygiene/vre/src/main/python/vre/model/Room.py b/spitalhygiene/vre/src/main/python/vre/model/Room.py new file mode 100644 index 0000000..119571d --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Room.py @@ -0,0 +1,153 @@ +from datetime import datetime +from Bed import Bed +import logging + + +class Room: + ''' + Models a room in the hospital and contains lists of moves and appointments that happened in this room. + ''' + def __init__(self, name): + """ + Note that most rooms will not have an ID ! (for some reason yet to be discovered) + + :param name: Room name + """ + self.name = name + self.ids = [] + self.ward = None + self.moves = [] + self.appointments = [] + self.beds = dict() + + def add_move(self, move): + ''' + Add a Move from SAP IS-H to this room. + In case the Move has information about the bed, the Bed is added to the room (if not yet exists). + :param move: Move + :return: + ''' + self.moves.append(move) + if move.bett is not None and move.bett is not "": + if self.beds.get(move.bett, None) is None: + b = Bed(move.bett) + self.beds[move.bett] = b + self.beds[move.bett].add_move(move) + + def add_id(self, id, system): + """ + Adds an (id, system) tuple to the Room().ids attribute list if id has not already been added - this prevents duplicate entries. + This list should be useful in determining whether or not there are overlapping room names among different hospital systems. + + :param id: id string + :param system: system string + """ + if id not in [id_tuple[0] for id_tuple in self.ids]: + self.ids.append( (str(id), str(system)) ) + + def add_appointment(self, appointment): + ''' + Add an appointment from RAP to this room. + :param appointment: Appointment + :return: + ''' + self.appointments.append(appointment) + + def add_ward(self, ward): + """ + Updates the self.ward attribute. + + :param ward: ward object to be set as the new attribute. + """ + self.ward = ward + + def get_ids(self): + """ + Returns all ids in the self.ids attribute. + :return: a @-delimited list of [room_id]_[system] for all (id, system) tuples in the self.ids attribute list, or None if the list is empty. + """ + if len(self.ids) == 0: + return None + else: + return '@'.join([each_tuple[0] + '_' + each_tuple[1] for each_tuple in self.ids]) + + def get_moves_during(self, start_dt, end_dt): + ''' + List of moves that overlap with the start_dt, end_dt time interval. + :param start_dt: datetime.datetime + :param end_dt: datetime.datetime + :return: List of Move + ''' + overlapping_moves = [] + for move in self.moves: + e_dt = move.bwe_dt if move.bwe_dt is not None else datetime.now() + if e_dt >= start_dt and move.bwi_dt <= end_dt: + overlapping_moves.append(move) + return overlapping_moves + + def create_room_id_map(lines, rooms): + ''' + Initializes the dictionary mapping room ids to Room() objects based on the provided csv file. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: V_DH_DIM_RAUM_CUR + ["RAUMID", "RAUMNAME"] + ["128307", "Kollmann Zahraa [00025783]"] + ["80872"," Audiometrie"] + + The function will also update the provided rooms dictionary. + + :param rooms: Dictionary mapping room names to Room() objects --> {'BH N 129' : Room(), ... } + :return: Dictionary mapping room ids to Room() objects --> {'127803' : Room(), ... } + ''' + logging.debug("create_room_id_map") + room_id_map = dict() + for line in lines: + room_obj = Room(line[1]) + room_obj.add_id(id = line[0], system = 'Polypoint') + # Update the room_id_map dictionary + room_id_map[line[0]] = Room(line[1]) + # Update the rooms dictionary + if line[1] not in rooms.keys(): + rooms[line[1]] = room_obj + else: + rooms[line[1]].add_id(id = line[0], system = 'Polypoint') + + logging.info(f"{len(room_id_map)} rooms created") + return room_id_map + + def add_room_to_appointment(lines, appointments, rooms): + """ + Reads room data from the csv file and adds the created Room() to an Appointment() in appointments and vice versa. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: V_DH_FACT_TERMINRAUM --> LEFT JOIN V_DH_DIM_RAUM --> ON [V_DH_FACT_TERMINRAUM].RAUMID = V_DH_DIM_RAUM_CUR.RAUMID + ["TERMINID", "RAUMID", "RAUMNAME", "TERMINSTART_TS", "TERMINENDE_TS", "DAUERINMIN"] + ["2295658", "11190", "P2 A534", "2007-12-10 08:45:00.0000", "2007-12-10 10:15:00.0000", "90.000000"] + ["2410965", "61994", "C 316", "2008-02-21 14:00:00.0000", "2008-02-21 15:00:00.0000", "60.000000"] + + :param appointments: Dictionary mapping appointment ids to Appointment() objects --> { '36830543' : Appointment(), ... } + :param room_id_map: Dictionary mapping room ids to Room() object --> {'127803' : Room(), ... } + :param rooms: Dictionary mapping room names to a Room() object --> {'BH N 125' : Room(), ... } + """ + logging.debug("add_room_to_appointment") + nr_appointments_not_found = 0 + nr_rooms_not_found = 0 + nr_ok = 0 + for line in lines: + appointment_id = line[0] + room_id = line[1] + room_name = line[2] + if appointments.get(appointment_id, None) is None: + nr_appointments_not_found += 1 + continue + # if room_id_map.get(room_id, None) is None: + # nr_rooms_not_found += 1 + # continue + # name = room_id_map[room_id] + if rooms.get(room_name, None) is None: + new_room = Room(room_name) + new_room.add_id(id = room_id, system = 'Polypoint' ) + rooms[room_name] = new_room + rooms[room_name].add_appointment(appointments[appointment_id]) + appointments[appointment_id].add_room(rooms[room_name]) + nr_ok += 1 + logging.info(f"{nr_ok} rooms added to appointments, {nr_appointments_not_found} appointments not found, {nr_rooms_not_found} rooms not found") diff --git a/spitalhygiene/vre/src/main/python/vre/model/Surgery.py b/spitalhygiene/vre/src/main/python/vre/model/Surgery.py new file mode 100644 index 0000000..1c01e00 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Surgery.py @@ -0,0 +1,62 @@ +from datetime import datetime +import logging + +class Surgery: + ''' + A surgery maps a case to a chop code. + Table schema: + [LFDBEW],[ICPMK],[ICPML],[ANZOP],[BGDOP],[LSLOK],[STORN],[FALNR],[ORGPF] + ''' + def __init__(self, LFDBEW,ICPMK,ICPML,ANZOP,BGDOP,LSLOK,STORN,FALNR,ORGPF): + self.bgd_op = datetime.strptime(BGDOP, "%Y-%m-%d") # date of surgery + self.lfd_bew = LFDBEW + self.icpmk = ICPMK # catalog ID + self.icpml = "Z" + ICPML # chop code (comes without leading Z!) + self.anzop = ANZOP # ?? + self.lslok = LSLOK # ?? + self.fall_nr = FALNR # case ID in SAP! + self.storn = STORN # cancelled ("" or "X")? + self.org_pf = ORGPF # ward + + self.chop = None + + def add_surgery_to_case(lines, cases, chops): + ''' + Creates Surgery() objects from a csv reader, and performs the following: + --> Finds the CHOP code in the chops dict (given the chop code and catalog id, which is read from the csv) + --> Finds the case with the correct case id and adds the surgery to the case. + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: + >> TABLE NAME: LA_ISH_NICP + ["LFDBEW", "ICPMK", "ICPML", "ANZOP", "BGDOP", "LSLOK", "STORN", "FALNR", "ORGPF"] + ["0", "10", "96.08", "0", "2011-12-23", "", "", "0003807449", "Q MITTE"] + ["0", "10", "87.41.00", "0", "2011-12-27", "", "", "0003807449", "Q MITTE"] + + :param cases: Dictionary mapping case ids to Case() objects --> {"0003536421" : Case(), "0003473241" : Case(), ...} + :param chops: Dictionary mapping the chopcode_katalogid entries to Chop() objects --> { 'Z39.61.10_11': Chop(), ... } + ''' + logging.debug("add_surgery_to_case") + nr_chop_not_found = 0 + nr_case_not_found = 0 + nr_surgery_storniert = 0 + nr_ok = 0 + for line in lines: + surgery = Surgery(*line) + if surgery.storn == 'X': # ignore 'stornierte' surgeries + nr_surgery_storniert += 1 + continue + chop = chops.get(surgery.icpml + "_" + surgery.icpmk, None) + case = cases.get(surgery.fall_nr, None) + if chop is not None: + surgery.chop = chop + else: + nr_chop_not_found += 1 + continue + if case is not None: + case.add_surgery(surgery) + chop.add_case(case) + else: + nr_case_not_found += 1 + continue + nr_ok += 1 + logging.info(f"{nr_ok} ok, {nr_case_not_found} cases not found, {nr_chop_not_found} chop codes not found, {nr_surgery_storniert} surgeries storniert") + diff --git a/spitalhygiene/vre/src/main/python/vre/model/Ward.py b/spitalhygiene/vre/src/main/python/vre/model/Ward.py new file mode 100644 index 0000000..463c331 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/model/Ward.py @@ -0,0 +1,19 @@ +from datetime import datetime + +class Ward: + def __init__(self, name): + self.name = name + self.moves = [] + self.appointments = [] + + def add_move(self, move): + self.moves.append(move) + + def get_moves_during(r, start_dt, end_dt): + overlapping_moves = [] + for m in r.moves: + e_dt = m.bwe_dt if m.bwe_dt is not None else datetime.now() + if e_dt >= start_dt and m.bwi_dt <= end_dt: + overlapping_moves.append(m) + return overlapping_moves + diff --git a/spitalhygiene/vre/src/main/python/vre/model/__init__.py b/spitalhygiene/vre/src/main/python/vre/model/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/spitalhygiene/vre/src/main/python/vre/networkx_graph.py b/spitalhygiene/vre/src/main/python/vre/networkx_graph.py new file mode 100644 index 0000000..417d0c0 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/networkx_graph.py @@ -0,0 +1,1257 @@ +# -*- coding: utf-8 -*- +"""This script contains the code to run statistics on various types of network models that will be used as proposed by +Theus (see README.md for more details). + +**Surface Model**: This model assumes that VRE is transmitted on "surfaces", and contains all potential transmitting +surfaces as nodes. These currently include: + +- Patients +- Employees +- Rooms +- Devices + +Edges are based on contact between two nodes if the contact (e.g. a patient being in a particular room) has occurred +during a patient's relevant stay. + +**Handshake Model**: This model assumes that VRE is transmitted via "handshakes", and contains only Patients as nodes. +In contrast to the Surface model, edges in this model correspond to the transmission vectors, and represent common rooms +or employees via which two patients may have been (indirectly) in contact with each other. + +----- +""" + +import copy +import os +import networkx as nx +import logging +import datetime +import itertools +import json +import random +from collections import Counter + + +def create_model_snapshots(orig_model, snapshot_dt_list): + """Creates model snapshots based on the datetime.datetime() values provided in snapshot_dt_list. + + Note: + For obvious reasons, all of the values provided must be smaller (earlier than) orig_model.snapshot_dt (i.e. + the snapshot date of the model used as a basis for the other models). + + Args: + orig_model (surface_model): Original surface_model() object that will be used as starting point for all + subsequent snapshots + snapshot_dt_list (list): List of dt.dt() objects, all smaller than self.snapshot_dt + + Returns: + list: List of independent surface_model() objects corresponding to the various model snapshots in increasing + order (i.e. the oldest snapshot is first in the list). The last entry in the returned list contains orig_model, + meaning the list has length `len(snapshot_dt_list) + 1`. If no snapshot creation is possible, ``None`` is + returned instead. + """ + if orig_model.snapshot_dt is None: + logging.error('Please add data to the model before taking snapshots !') + return None + if True in [dt_value > orig_model.snapshot_dt for dt_value in snapshot_dt_list]: + logging.error('All snapshot values must be smaller than the snapshot time of the current model !') + return None + sorted_snapshots = sorted(snapshot_dt_list, reverse=True) + model_list = [copy.deepcopy(orig_model)] + for dt_value in sorted_snapshots: + logging.info(f"Creating snapshot for {dt_value.strftime('%d.%m.%Y %H:%M:%S')}...") + temp_model = copy.deepcopy(orig_model) + temp_model.trim_model(snapshot_dt=dt_value) + temp_model.remove_isolated_nodes(silent = True) + model_list.append(temp_model) + logging.info(f'--> Success ! Snapshot contains {len(temp_model.S_GRAPH.nodes())} nodes ' + f'and {len(temp_model.S_GRAPH.edges())} edges') + # Reverse order of models (oldest snapshot should be the first entry) and return the list of snapshot models + model_list.reverse() + return model_list + + +class surface_model: + """Represents the `Surface Model` graph in networkx. + + Nodes can be one of: + + - Patients :math:`\\longrightarrow` added with attribute dict ``{'type' : "Patient" }`` + - Employees :math:`\\longrightarrow` added with attribute dict ``{'type' : "Employee" }`` + - Rooms :math:`\\longrightarrow` added with attribute dict ``{'type' : "Room" }`` + - Devices :math:`\\longrightarrow` added with attribute dict ``{'type' : "Device" }`` + + A few details on how the model graph is set up: + + - All node objects will be represented in the form of string objects, where unique identifiers are as follows: + + - Patients :math:`\\longrightarrow` ``patient ID`` + - Rooms: :math:`\\longrightarrow` ``room name`` + - Employees: :math:`\\longrightarrow` ``employee ID`` + - Devices: :math:`\\longrightarrow` ``Device ID`` + + - All node objects will have at least two attributes: + + - ``type`` (see docstring above) + - ``cluster`` (for visual representation with Alchemy) --> added via the function `add_node_clustering()` + + - All edge objects will have at least the following attributes: + + - ``from`` :math:`\\longrightarrow` indicates start of the interaction (a dt.dt() object) + - ``to`` :math:`\\longrightarrow` indicates end of the interaction (a dt.dt() object) + - ``type`` :math:`\\longrightarrow` indicates what node types are linked by this edge, e.g. "Patient-Room", + "Patient-Employee", etc. + - ``origin`` :math:`\\longrightarrow` indicates the source that was used to add this edge (e.g. "Move", + 'Appointment", etc.) + + - Edge descriptions always contain the nodes in alphabetical order, i.e. Device-Room (NOT Room-Device), + Employee-Patient (NOT Patient-Employee), etc. + - Each node is only present once + - Edges from a node to itself are not supported + - Multiple parallel edges between two nodes are supported (:math:`\\longrightarrow` MultiGraph) + - Edges do not have associated weights, but have at least one attribute "type" (e.g. Patient-Room, Employee-Patient) + - The edge attribute "type" is **not** sorted alphabetically, and instead features the following 6 variations: + + - Patient-Room + - Device-Patient + - Employee-Patient + - Device-Room + - Employee-Room + - Device-Employee + + - The graph is undirected + - The python built-in ``None``-type object should not be used for attributes according to the networkx docs. + Instead, unknown values will be set to "NULL" or "" + + """ + + def __init__(self, data_dir='.', edge_types=None): + """Initiates the graph in networkx (see class docstring for details). + + Args: + data_dir (str): Data directory in which to store output files from various data extraction functions. + edge_types (tuple): Tuple containing all edge types to include in the model (can be any combination of the 4 + node types). This value defaults to None, resulting in the inclusion of all edge types. + See class docstring for details. + """ + self.S_GRAPH = nx.MultiGraph() + # Flag indicating whether or not the self.add_edge_infection() function has been called on the graph + # -> introduces the "infected" attribute for edges + self.edges_infected = False + self.Nodes = { + 'Patient': set(), + 'Room': set(), + 'Device': set(), + 'Employee': set(), + } # Dictionary mapping to sets of respective node string identifiers + # Initiate various counters: + self.edge_add_warnings = 0 # Number of warnings encountered during the addition of edges to the network + self.room_add_warnings = 0 # Number of warnings encountered during the addition of room nodes + self.patient_add_warnings = 0 # Number of warnings encountered during the addition of patient nodes + self.employee_add_warnings = 0 # Number of warnings encountered during the addition of employee nodes + self.device_add_warnings = 0 # Number of warnings encountered during the addition of device nodes + + self.betweenness_centrality = None + # Changed to a dictionary mapping nodes to betweenness centrality scores once the + # self.export_node_betweenness() function is called + + self.data_dir = data_dir + + self.shortest_path_stats = False + # indicates whether shortest path statistics have been added to nodes via update_shortest_path_statistics() + + self.node_files_written = False # indicates whether node files (in JSON format) are present in self.data_dir + + self.snapshot_dt = None # time at which "snapshot" of the model is taken (important for "sub-snapshots") + + self.edge_types = ('Patient-Room', 'Device-Patient', 'Employee-Patient', 'Device-Room', 'Employee-Room', + 'Device-Employee') if edge_types is None else edge_types + # tuple containing types of edges to include in the network (or None --> includes edge types) + + ########################################################################## + # Class-specific Exceptions + ########################################################################## + class NodeBetweennessException(Exception): + """ Class-specific exception used for betweenness calculation functions. + """ + pass + + ########################################################################## + # Base Functions + ########################################################################## + @staticmethod + def parse_filename(filename, replace_char='@'): + """Parses filename and replaces problematic characters with replace_char. + + Args: + filename (str): Name of file to parse. + replace_char (str): Replacement problematic characters. + + Returns: + str: parsed (unproblematic) filename + """ + return filename.replace('/', replace_char) + + @staticmethod + def save_to_json(path_to_file, saved_object): + """Saves object to `path_to_file` in JSON format. + + Args: + path_to_file (str): Path to file to be saved (must include ``.json`` suffix) + saved_object: Object to be saved to file in JSON format. + """ + json.dump(saved_object, open(path_to_file, 'w')) + + @staticmethod + def load_from_json(path_to_file): + """Loads the .json file specified in `path_to_file`. + + Args: + path_to_file (str): Path to `.json` file to be loaded. + + Returns: + The object loaded from `path_to_file`. + """ + loaded_object = json.load(open(path_to_file)) # default mode for open(...) is 'r' + return loaded_object + + def identify_id(self, string_id): + """Checks whether a node with string_id exists in the network. + + Returns the type (e.g. 'Patient', 'Employee', etc.) of `string_id` in the network. If string_id does not exist + in the network, ``None`` is returned instead. + + Args: + string_id (str): string identifier of the node to be identified. + + Returns: + str or None: The type of node of string_id, or ``None`` if string_id is not found in the network. + """ + for key in self.Nodes.keys(): + if string_id in self.Nodes[key]: # self.Nodes[key] will be a set --> in operator performs very well + return key + return None + + def identify_node(self, node_id, node_type): + """Checks whether node_id is found in self.Nodes[node_type]. + + This function is more performant than identify_id(), since it already assumes that the node type of the string + to be identified is known. + + Args: + node_id (str): String identifier of the node to be identified. + node_type (str): Type of node to be identified (e.g. 'Patient') + + Returns: + bool: `True` if node_id is found in self.Nodes[node_type], `False` otherwise. + """ + if node_id in self.Nodes[node_type]: + return True + return False + + ########################################################################## + # Functions for expanding or reducing the graph + ########################################################################## + def new_generic_node(self, string_id, attribute_dict): + """Adds a new generic node to the graph. + + String_id will be used as the unique identifier, and all key-value pairs in attribute_dict as additional + information to add to the node. If a node already exists, only new entries in attribute_dict will be added to + it, but it will otherwise be left unchanged. + + Args: + string_id (str): string identifier for node + attribute_dict (dict): dictionary of key-value pairs containing additional information + """ + self.S_GRAPH.add_node(str(string_id), **attribute_dict) + + def new_patient_node(self, string_id, risk_dict, warn_log=False): + """Add a patient node to the network. + + Automatically sets the 'type' attribute to "Patient". Also adds risk_dict to the 'risk' attribute as defined + for patient nodes. It will also add an attribute 'vre_status' ('pos' or 'neg') depending on whether or not code + 32 is found in risk_dict. Note that if string_id is empty (''), no node will be added to the network and a + warning will be logged if warn_log is `True`. + + Args: + string_id (str): string identifier of patient to be added. + risk_dict (dict): dictionary mapping dt.dt() to Risk() objects corresponding to a patient's VRE screening + history. + warn_log (bool): flag indicating whether or not to log warning messages. + """ + if string_id == '': + if warn_log == True: + logging.warning('Empty patient identifier - node is skipped') + self.patient_add_warnings += 1 + return + risk_codes = [each_risk.screen_code for each_risk in risk_dict.values()] + self.S_GRAPH.add_node(str(string_id), type='Patient', risk=risk_dict, vre_status='pos' + if 32 in risk_codes else 'neg') + self.Nodes['Patient'].add(string_id) + + def new_room_node(self, string_id, ward=None, room_id=None, warn_log=False): + """Add a room node to the network. + + Automatically sets the 'type' attribute to "Room" and ward to the "ward" attribute, and sets room_id to either + the specified value or "NULL". Note that if string_id is empty (''), no node will be added to the network and a + warning will be logged if warn_log is `True`. + + Args: + string_id (str): string identifier of room to be added. + ward (str): name of ward of this room + room_id (str): room id (in string form) of this room + warn_log (bool): flag indicating whether or not to log warning messages. + """ + if string_id == '': + if warn_log == True: + logging.warning('Empty room identifier - node is skipped') + self.room_add_warnings += 1 + return + attribute_dict = {'ward': 'NULL' if ward is None else str(ward), 'room_id': 'NULL' + if room_id is None else str(room_id), 'type': 'Room'} + self.S_GRAPH.add_node(str(string_id), **attribute_dict) + self.Nodes['Room'].add(string_id) + + def new_device_node(self, string_id, name, warn_log=False): + """Add a device node to the network. + + Automatically sets the 'type' attribute to "Device". Note that if string_id is empty (''), no node will be + added to the network and a warning will be logged if warn_log is `True`. + + Args: + string_id (str): string identifier of device to be added. + name (str): name of device. + warn_log (bool): flag indicating whether or not to log warning messages. + """ + if string_id == '': + if warn_log == True: + logging.warning('Empty device identifier - node is skipped') + self.device_add_warnings += 1 + return + self.S_GRAPH.add_node(str(string_id), type='Device', name = name) + self.Nodes['Device'].add(string_id) + + def new_employee_node(self, string_id, warn_log=False): + """Add an employee node to the network. + + Automatically sets the 'type' attribute to "employee". Note that if string_id is empty (''), no node will be + added to the network and a warning will be logged if warn_log is `True`. + + Args: + string_id (str): string identifier of employee to be added. + warn_log (bool): flag indicating whether or not to log warning messages. + """ + if string_id == '': + if warn_log == True: + logging.warning('Empty employee identifier - node is skipped') + self.employee_add_warnings += 1 + return + self.S_GRAPH.add_node(str(string_id), type='Employee') + self.Nodes['Employee'].add(string_id) + + def new_edge(self, source_id, source_type, target_id, target_type, att_dict, log_warning=False): + """Adds a new edge to the network. + + The added edge will link source_id of source_type to target_id of target_type. Note that the edge will ONLY be + added if both source_id and target_id are found in the self.Nodes attribute dictionary. In addition, all + key-value pairs in att_dict will be added to the newly created edge. + + Args: + source_id (str): String identifying the source node + source_type (str): source_id type, which must be one of ['Patient', 'Room', 'Device', 'Employee'] + target_id (str): String identifying the target node + target_type (str): target_id type, which must be one of ['Patient', 'Room', 'Device', 'Employee'] + att_dict (dict): dictionary containing attribute key-value pairs for the new edge. + log_warning (bool): flag indicating whether or not to log a warning each time a faulty edge is encountered + """ + if self.identify_id(source_id) is None: + if log_warning == True: + logging.warning(f'Did not find node {source_id} of type {source_type} - no edge added') + self.edge_add_warnings += 1 + return + if self.identify_id(target_id) is None: + if log_warning == True: + logging.warning(f'Did not find node {target_id} of type {target_type} - no edge added') + self.edge_add_warnings += 1 + return + self.S_GRAPH.add_edge(source_id, target_id, **att_dict) + + def remove_isolated_nodes(self, silent=False): + """Removes all isolated nodes from the network. + + Isolated nodes are identified as having degree 0. + + Args: + silent (bool): Flag indicating whether or not to log progress (defaults to ``False``) + """ + node_degrees = [self.S_GRAPH.degree(each_node) for each_node in self.S_GRAPH.nodes] + if silent == False: + logging.info(f"##################################################################################") + logging.info('Removing isolated nodes:') + logging.info(f'--> Before processing, network contains {len(node_degrees)} total nodes, out of which ' + f'{node_degrees.count(0)} are isolated.') + remove_count = 0 + delete_list = [] + for each_node in self.S_GRAPH.nodes: + if self.S_GRAPH.degree(each_node) == 0: # degree of 0 indicates an isolated node + delete_list.append(each_node) + for node in delete_list: + self.S_GRAPH.remove_node(node) + remove_count += 1 + node_degrees_after = [self.S_GRAPH.degree(each_node) for each_node in self.S_GRAPH.nodes] + if silent == False: + logging.info(f'--> After processing, network contains {len(node_degrees_after)} total nodes, out of which ' + f'{node_degrees_after.count(0)} are isolated.') + + def trim_model(self, snapshot_dt): + """Trims the current model. + + Removes all edges for which the ``to`` attribute is larger than snapshot_dt, and updates the self.snapshot_dt + attribute. However, this function does NOT remove isolated nodes. + + Args: + snapshot_dt (dt.dt()): dt.dt() object specifying to which timepoint the model should be trimmed + """ + deleted_edges = [edge_tuple for edge_tuple in self.S_GRAPH.edges(data=True, keys=True) + if edge_tuple[3]['to'] > snapshot_dt] + # S_GRAPH.edges() returns a list of tuples of length 4 --> ('source_id', 'target_id', key, attr_dict) + self.S_GRAPH.remove_edges_from(deleted_edges) + self.snapshot_dt = snapshot_dt + + ################################################################################################################ + # Functions for updating attributes + ################################################################################################################ + def update_edge_attributes(self, edge_tuple, attribute_dict): + """Updates the edge identified in edge_tuple. + + Add all key-value pairs in attribute_dict. Existing attributes will be overwritten. + + Args: + edge_tuple (tuple): Tuple of length 3 identifying the edge :math:`\\longrightarrow` (source_id, + target_id, key) (`key` is required to uniquely identify MultiGraph() edges) + attribute_dict (dict): dictionary of key-value pairs with which to update the edge + """ + attrs = {edge_tuple : attribute_dict} + # to update a specific edge, the dictionary passed to set_edge_attributes() must be formatted + # as --> { ('bla', 'doodel', 0) : {'newattr' : 'somevalue'} } + nx.set_edge_attributes(self.S_GRAPH, attrs) + + def update_node_attributes(self, node_id, attribute_dict): + """Updates the node identified in node_id. + + The node will be updated with all key-value pairs in attribute_dict. Note that existing attributes will be + overwritten with the values in attribute_dict. + + Args: + node_id (str): string identifier for the node + attribute_dict (dict): dictionary of key-value pairs with which the node will be updated + """ + attrs = {node_id : attribute_dict} + # to update a specific edge, the dictionary passed to set_node_attributes() must be formatted + # as --> { 'node_id' : {'newattr' : 'somevalue'} } + nx.set_node_attributes(self.S_GRAPH, attrs) + + def add_edge_infection(self): + """Sets "infected" attribute to all edges. + + This function will iterate over all edges in the network and set an additional attribute ``infected``, which + will be set to ``True`` if it connects to a patient node for which the ``vre_status`` attribute is set to + ``pos``. For all other edges, this attribute will be set to ``False``. + """ + logging.info(f"##################################################################################") + logging.info('Adding infection data to network edges...') + neg_infect_count = 0 + pos_infect_count = 0 + error_count = 0 + for each_edge in self.S_GRAPH.edges(data=True, keys=True): + # will yield a list of tuples of length 4 --> (source_id, target_id, key, attribute_dict) + try: + edge_types = [self.identify_id(each_edge[i]) for i in range(0, 2)] + if None in edge_types: + # will yield a list of length 2 --> [source_id_type, target_id_type] (e.g. ['Patient', 'Room'] ) + logging.warning(f"Encountered an edge for which at least one node could not be identified") + self.update_edge_attributes(edge_tuple=(each_edge[0], each_edge[1], each_edge[2]), + attribute_dict={'infected': False}) + continue + if 'Patient' not in edge_types: # indicates an edge between two non-patient nodes + self.update_edge_attributes(edge_tuple=(each_edge[0], each_edge[1], each_edge[2]), + attribute_dict={'infected' : False}) + neg_infect_count += 1 + else: + # indicates an edge of one of the following types: + # Patient-Room, Patient-Device or Patient-Employee (patient id is always in each_edge[0]) + pat_index = edge_types.index('Patient') + # returns the index (0 or 1) containing the patient node (can either be source_id or target_id) + if self.S_GRAPH.node[each_edge[pat_index]]['vre_status'] == 'neg': + self.update_edge_attributes(edge_tuple=(each_edge[0], each_edge[1], each_edge[2]), + attribute_dict={'infected': False}) + neg_infect_count += 1 + else: + self.update_edge_attributes(edge_tuple=(each_edge[0], each_edge[1], each_edge[2]), + attribute_dict={'infected': True}) + pos_infect_count += 1 + except Exception as e: # e is currently not used + self.update_edge_attributes(edge_tuple=(each_edge[0], each_edge[1], each_edge[2]), + attribute_dict={'infected': False}) + error_count += 1 + logging.warning(f'Encountered {error_count} errors during the identification of infected patient nodes') + logging.info(f"Successfully added 'infected' status to network edges ({pos_infect_count} infected, " + f"{neg_infect_count} uninfected edges)") + self.edges_infected = True + + def update_shortest_path_statistics(self, focus_nodes=None, approximate=False, max_path_length=None): + """Prerequisite function for calculating betweenness centrality. + + Adds new attributes to all nodes in focus_nodes, where each attribute is a pair of nodes (sorted alphabetically) + with a ``SP-`` prefix to tuples of length 2 containing (shortest_paths_through_this_node, total_shortest_paths) + For example: + + ``{ 'SP-Node1-Node2': (2, 5) , 'SP-Node1-Node3': (1, 8), ... } }`` + + Note: + This may result in a lot of additional attributes for nodes which are integral to the network. This ap- + proach is chosen because the networkx module does not allow updates to a dict-of-a-dict type + of attributes - i.e. if these attributes were to be combined in a 1st-level key 'shortest-paths', the entire + content would have to be copied every time a new node-pair attribute is added, which would make the function + extremely inefficient. + + This is an important prerequisite function for the calculation of betweenness centrality. + + Args: + focus_nodes (list): list of node IDs. If set to ``None`` (the default), all nodes in the network will be + considered. **WARNING: this may be extremely ressource-intensive !** + approximate (bool): Flag indicating whether to consider all shortest paths in the network + (``False``, default) or approximate the betweenness statistic using the + `max_path_length` argument. Note that if this is set to `False`, attributes of all + nodes will be written **to file** so as to avoid memory overflows. This requires a + preceding call to `self.write_node_files()`. + max_path_length (int): Maximum path length to consider for pairs of nodes when `approximate` == ``True``. + If set to ``None`` (default), all possible shortest paths will be considered. + """ + target_nodes = focus_nodes if focus_nodes is not None else self.S_GRAPH.nodes + node_combinations = list(itertools.combinations(target_nodes, 2)) + # Returns a list of tuples containing all unique pairs of nodes in considered_nodes + + logging.info(f'--> Adding shortest path statistics considering {len(target_nodes)} nodes yielding ' + f'{len(node_combinations)} combinations.') + logging.info(f"Approximate set to {approximate}, maximum path length set to {max_path_length}") + for count, combo_tuple in enumerate(node_combinations): + if count % 100 == 0: + logging.info(f" <> Processed {count} combinations") + if nx.has_path(self.S_GRAPH, combo_tuple[0], combo_tuple[1]) is False: + continue # indicates a node pair in disconnected network parts + # measure = datetime.datetime.now() + if approximate is False: # This will write required prerequisites to the node files in self.data_dir + if self.node_files_written is False: + raise self.NodeBetweennessException('Missing a required call to self.write_node_files() !') + all_shortest_paths = list(nx.all_shortest_paths(self.S_GRAPH, source=combo_tuple[0], + target=combo_tuple[1])) + # Remove the first and last node (i.e. source and target) of all shortest paths + trim_short_paths = [path_list[1:(len(path_list)-1)] for path_list in all_shortest_paths] + involved_nodes = [node for sublist in trim_short_paths for node in sublist] + node_counts = Counter(involved_nodes) + for each_key in node_counts: + json_filepath = os.path.join(self.data_dir, self.parse_filename(each_key) + '.json') + node_dict = self.load_from_json(path_to_file=json_filepath) # Returns type dictionary + if 'BW-Stats' not in node_dict.keys(): + node_dict['BW-Stats'] = [(node_counts[each_key], len(all_shortest_paths))] # -> list of tuples + else: + node_dict['BW-Stats'].append((node_counts[each_key], len(all_shortest_paths))) + # Save node_dict back to file + self.save_to_json(path_to_file=json_filepath, saved_object=node_dict) + else: + shortest_pair_path = nx.shortest_path(self.S_GRAPH, source=combo_tuple[0], target=combo_tuple[1]) + if max_path_length is not None and len(shortest_pair_path) > max_path_length: + continue # indicates a path too long to be considered relevant for transmission + # If shortest paths are "short enough", calculate the exact measure + all_shortest_paths = list(nx.all_shortest_paths(self.S_GRAPH, source=combo_tuple[0], + target=combo_tuple[1])) + # Remove the first and last node (i.e. source and target) of all shortest paths + trim_short_paths = [path_list[1:(len(path_list)-1)] for path_list in all_shortest_paths] + involved_nodes = [node for sublist in trim_short_paths for node in sublist] + node_counts = Counter(involved_nodes) + sorted_pair = sorted([combo_tuple[0], combo_tuple[1]]) + update_attr_dict = {each_key: {'SP-' + sorted_pair[0] + '-' + sorted_pair[1]: (node_counts[each_key], + len(all_shortest_paths))} + for each_key in node_counts if each_key not in sorted_pair} + # Then update node attributes + nx.set_node_attributes(self.S_GRAPH, update_attr_dict) + # Write it all to log + logging.info(f"Successfully added betweenness statistics to the network !") + # Adjust the self.shortest_path_stats + self.shortest_path_stats = True + + ########################################################################## + # Customized Network Functions + ########################################################################## + def inspect_network(self): + """Important inspect function for the graph. + + An important function that will inspect all properties of the network and return diagnostic measures on the + "quality". This includes: + + - Total number of nodes in the network + - Number of isolated nodes in the network + - Number of nodes in the network of type: + + - Patient + - Device + - Employee + - Room + + - Total number of edges in the network + - Number of edges in the network of type: + + - Patient-Device + - Patient-Room + - Patient-Employee + - Employee-Device + - Employee-Room + - Device-Room + + - Number of improperly formatted edges. These include: + + - Edges for which at least one node is empty, i.e. "" + - Edges for which any one of the ``from``, ``to``, ``type``, ``origin``, and ``infected`` + (if self.edge_infected == ``True``) attributes are not present + + All result statistics are printed to log. + """ + logging.info(f"###############################################################") + logging.info(f"Running network statistics...") + + all_nodes = self.S_GRAPH.nodes(data=True) # list of tuples of ('source_id', key, {attr_dict } ) + node_degrees = [self.S_GRAPH.degree(each_node) for each_node in self.S_GRAPH.nodes] + all_edges = self.S_GRAPH.edges(data=True, keys=True) # tuple list -> ('source', 'target', key, {attr_dict } ) + + # Overall network statistics + logging.info(f'--> Model Snapshot date: {self.snapshot_dt.strftime("%d.%m.%Y %H:%M:%S")}') + logging.info(f"--> Total {len(all_nodes)} nodes, out of which {node_degrees.count(0)} are isolated") + logging.info(f"--> Total {len(all_edges)} edges") + logging.info('------------------------------') + + # Extract specific node statistics + nbr_pat_nodes = len(['_' for node_data_tuple in all_nodes if node_data_tuple[1]['type'] == 'Patient']) + nbr_dev_nodes = len(['_' for node_data_tuple in all_nodes if node_data_tuple[1]['type'] == 'Device']) + nbr_emp_nodes = len(['_' for node_data_tuple in all_nodes if node_data_tuple[1]['type'] == 'Employee']) + nbr_room_nodes = len(['_' for node_data_tuple in all_nodes if node_data_tuple[1]['type'] == 'Room']) + accounted_for = nbr_pat_nodes + nbr_dev_nodes + nbr_emp_nodes + nbr_room_nodes + logging.info('Node overview:') + logging.info(f"--> {nbr_pat_nodes} Patient nodes") + logging.info(f"--> {nbr_dev_nodes} Device nodes") + logging.info(f"--> {nbr_emp_nodes} Employee nodes") + logging.info(f"--> {nbr_room_nodes} Room nodes") + logging.info(f"--> TOTAL: {accounted_for} nodes ({len(all_nodes) - accounted_for} out of " + f"{len(all_nodes)} nodes not accounted for)") + logging.info('------------------------------') + + # Extract specific edge statistics + type_count_dict = {} + + faulty_sourceordest_id = 0 # Counts edges for which the source or target id are wrongly formatted + nbr_missing_attr = 0 # Counts edges which are missing at least one of the attributes + nbr_ok = 0 # Counts edges passing all tests + + test_attribute_keys = ['type', 'from', 'to', 'origin'] if self.edges_infected == False \ + else ['type', 'from', 'to', 'origin', 'infected'] + + for each_edge in all_edges: + # Check source and target id + if each_edge[0] == '' or each_edge[1] == '': + faulty_sourceordest_id += 1 + continue + attr_keys = each_edge[3].keys() + if len([entry for entry in test_attribute_keys if entry not in attr_keys]) > 0: + # indicates at least one missing attribute + nbr_missing_attr += 1 + continue + # If everything is ok, update type_count_dict + nbr_ok += 1 + if each_edge[3]['type'] not in type_count_dict: + type_count_dict[each_edge[3]['type']] = 1 + else: + type_count_dict[each_edge[3]['type']] += 1 + type_count_keys = sorted(list(type_count_dict.keys())) + # Write all remaining results to log + logging.info(f"--> {faulty_sourceordest_id} edges with a faulty source or target id") + logging.info(f"--> {nbr_missing_attr} edges missing at least one attribute") + logging.info(f"--> {nbr_ok} edges ok:") + for each_key in type_count_keys: + logging.info(f" >> {type_count_dict[each_key]} edges of type {each_key}") + accounted_for = sum([value for value in type_count_dict.values()]) + logging.info(f"--> TOTAL: {accounted_for} edges ({len(all_edges) - accounted_for} out of {len(all_edges)} " + f"edges not accounted for)") + logging.info('------------------------------') + + # Number of positive patients in the network + nbr_pos_pat = len(['_' for node_data_tuple in all_nodes if node_data_tuple[1]['type'] == 'Patient' and + node_data_tuple[1]['vre_status'] == 'pos']) + logging.info(f"--> {nbr_pos_pat} VRE-positive Patients in the network") + logging.info('------------------------------') + + # Graph connectivity + logging.info(f"--> Graph onnected: {nx.is_connected(self.S_GRAPH)}") + logging.info(f"###############################################################") + + def add_network_data(self, patient_dict, subset='relevant_case', snapshot=datetime.datetime.now()): + """Adds nodes and edges data to the network. + + Nodes and edges are added based on the data in patient_dict according to the subset specified (see description + of parameters below). + + Args: + patient_dict (dict): Dictionary containing all data required to build the graph. Please refer to + "Patient_Data_Overview.dov" for details on this argument. + subset (str): Subset of data to be used, can be one of: + + - ``relevant_case`` :math:`\\longrightarrow` includes patients with a relevant case + (regardless of involvement in VRE screenings) and the data of relevant cases + - ``risk`` :math:`\\longrightarrow` includes patients with an associated risk (i.e. + at least one VRE screening) and data of relevant cases + + snapshot (dt.dt()): datetime.datetime() object specifying to which point in time data are to be + imported. Defaulting to the time of execution, this parameter can be used to create + a "snapshot" of the model, and will *ignore* (i.e. not add) edges in patient_dict + for which the 'to' attribute is larger than this parameter. Note that all nodes from + patient_dict will be added, but most "new" nodes will be created in isolation. And + since a call to this function is usually followed by a call to + *remove_isolated_nodes()*, these isolated nodes will then be stripped from the + network. + """ + logging.info(f"Filter set to: {subset}") + logging.info(f"Snapshot created at: {snapshot.strftime('%d.%m.%Y %H:%M:%S')}") + self.snapshot_dt = snapshot + ############################################################# + # --> Measures for the created network + ############################################################# + # ### General measures + nbr_pat_no_rel_case = 0 # Counts patients without... + nbr_pat_rel_case = 0 # ... and with a relevant case + nbr_room_no_id = 0 # Counts number of unidentifyable... + nbr_room_id = 0 # ... and identifyable rooms + nbr_app = 0 # number of appointments parsed + + # ### Edge measures + nbr_pat_emp = 0 # number of Patient-Employee edges + nbr_pat_room = 0 # number of Patient-Room edges + nbr_pat_device = 0 # number of Patient-Device edges + nbr_room_device = 0 # number of Room-Device edges + nbr_device_emp = 0 # number of Device-Employee edges + nbr_emp_room = 0 # number of Employee-Room edges + + for each_pat in patient_dict['patients'].values(): + # Apply subset filter here --> relevant_case + if subset == 'relevant_case': + pat_rel_case = each_pat.get_relevant_case() # Returns a Case() object or None + if pat_rel_case is None: + nbr_pat_no_rel_case += 1 + continue + nbr_pat_rel_case += 1 + this_pat_id = pat_rel_case.patient.patient_id + if this_pat_id == '': + logging.warning('Encountered empty patient ID !') + continue + # Add patient node + self.new_patient_node(str(this_pat_id), risk_dict=each_pat.risks) + ######################################### + # --> Step 1: Add rooms based on Move() objects to the network + ######################################### + for each_move in pat_rel_case.moves.values(): # iterate over all moves in a Patient's relevant case + zimmer = each_move.zimmr # will either be the room's name or None + this_ward = each_move.ward.name # will either be the ward's name or None + if zimmer is None: # --> If room is not identified, add it to the 'generic' Room node "Room_Unknown" + if "Room_Unknown" not in self.S_GRAPH.nodes: + self.new_room_node('Room_Unknown') + this_room = 'Room_Unknown' + nbr_room_no_id += 1 + else: # --> room is identified + this_room = each_move.zimmr + # Add room node - this will only overwrite attributes if node is already present + # --> does not matter since room_id and ward are the same + self.new_room_node(each_move.zimmr, ward = this_ward, room_id=each_move.room.get_ids() + if each_move.room is not None else None) + # .get_ids() will return a '@'-delimited list of [room_id]_[system] entries, or None + nbr_room_id += 1 + # Add Patient-Room edge if it's within scope of the current snapshot + edge_dict = {'from': each_move.bwi_dt, 'to': each_move.bwe_dt, + 'type': 'Patient-Room', 'origin': 'Move'} + if edge_dict['to'] < snapshot: + self.new_edge(str(this_pat_id), 'Patient', this_room, 'Room', att_dict=edge_dict) + ######################################### + # --> Step 2: Add Rooms, devices and employees based on the relevant Case().appointments + # This will add various edges in the network, since all Appointments contain information on the + # patient, employees, devices and rooms: + # --> Device-Patient + # --> Patient-Room + # --> Employee-Patient + # --> Device-Employee + # --> Employee-Room + # --> Device-Room + # (Remember: nodes in edge specifications are sorted alphabetically) + ######################################### + for each_app in pat_rel_case.appointments: + nbr_app += 1 + duration_from = each_app.termin_datum + duration_to = each_app.termin_datum + datetime.timedelta(hours=each_app.dauer_in_min / 60) + edge_attributes = {'from': duration_from, 'to': duration_to, 'origin': 'Appointment'} + # 'type' key will be added during the creation of edges, see below + device_list = [] + employee_list = [] + room_list = [] + #################################### + # --> ADD NODES + #################################### + # --> Add device nodes + for each_device in each_app.devices: + self.new_device_node(str(each_device.geraet_id), name=str(each_device.geraet_name)) + device_list.append(str(each_device.geraet_id)) + # --> Add employee nodes + for each_emp in each_app.employees: + self.new_employee_node(str(each_emp.mitarbeiter_id)) + employee_list.append(str(each_emp.mitarbeiter_id)) + # --> Add Room nodes + for each_room in each_app.rooms: + self.new_room_node(string_id=each_room.name, ward=each_room.ward.name if each_room.ward is not None else None, room_id=each_room.get_ids()) + room_list.append(each_room.name) + #################################### + # --> ADD EDGES based on specifications in self.edge_types + #################################### + # --> Device-Patient + if 'Device-Patient' in self.edge_types: + edge_attributes['type'] = 'Device-Patient' + for device in device_list: + if edge_attributes['to'] < snapshot: + self.new_edge(this_pat_id, 'Patient', device, 'Device', att_dict=edge_attributes) + nbr_pat_device += 1 + # --> Patient-Room + if 'Patient-Room' in self.edge_types: + edge_attributes['type'] = 'Patient-Room' + for room in room_list: + if edge_attributes['to'] < snapshot: + self.new_edge(this_pat_id, 'Patient', room, 'Room', att_dict=edge_attributes) + nbr_pat_room += 1 + # --> Employee-Patient + if 'Employee-Patient' in self.edge_types: + edge_attributes['type'] = 'Employee-Patient' + for emp in employee_list: + if edge_attributes['to'] < snapshot: + self.new_edge(this_pat_id, 'Patient', emp, 'Employee', att_dict=edge_attributes) + nbr_pat_emp += 1 + # --> Device-Employee + if 'Device-Employee' in self.edge_types: + edge_attributes['type'] = 'Device-Employee' + for emp in employee_list: + for device in device_list: + if edge_attributes['to'] < snapshot: + self.new_edge(emp, 'Employee', device, 'Device', att_dict=edge_attributes) + nbr_device_emp += 1 + # --> Employee-Room + if 'Employee-Room' in self.edge_types: + edge_attributes['type'] = 'Employee-Room' + for emp in employee_list: + for room in room_list: + if edge_attributes['to'] < snapshot: + self.new_edge(emp, 'Employee', room, 'Room', att_dict=edge_attributes) + nbr_emp_room += 1 + # --> Device-Room + if 'Device-Room' in self.edge_types: + edge_attributes['type'] = 'Device-Room' + for device in device_list: + for room in room_list: + if edge_attributes['to'] < snapshot: + self.new_edge(device, 'Device', room, 'Room', att_dict=edge_attributes) + nbr_room_device += 1 + ######################################### + logging.info(f"##################################################################################") + logging.info(f"Encountered {nbr_room_no_id} moves without associated room, {nbr_room_id} rooms identified.") + logging.info('------------------------------------------------------------------') + total_warnings = self.room_add_warnings + self.employee_add_warnings + self.device_add_warnings + \ + self.patient_add_warnings + if total_warnings > 0: + logging.warning(f'Encountered the following errors during the addition of nodes to the network:') + logging.warning(f'--> {self.room_add_warnings} errors during the addition of room nodes to the network') + logging.warning(f'--> {self.employee_add_warnings} errors during the addition of employee nodes to the ' + f'network') + logging.warning(f'--> {self.patient_add_warnings} errors during the addition of patient nodes to the ' + f'network') + logging.warning(f'--> {self.device_add_warnings} errors during the addition of device nodes to the network') + if self.edge_add_warnings > 0: + logging.info('------------------------------------------------------------------') + logging.warning(f'Encountered {self.edge_add_warnings} warnings while adding edges to the network.') + logging.info(f"##################################################################################") + # Log "global" statistics + self.inspect_network() + + ################################################################################################################ + # Data Export Functions + ################################################################################################################ + def export_patient_degree_ratio(self, csv_sep=';', export_path=None): + """Calculates and exports patient degree ratio for all nodes in the network. + + The patient degree ratio is defined for a single node_x as: + + number of infected edges between node_x and patients / number of total edges between node_x and patients + + Args: + csv_sep (str): Separator for created csv file. + export_path (str): Path to which export file will be written. If set to `None` (the default), the + exported file will be written to `self.data_dir`. + + The result file will be written to the path [self.data_dir]/[self.snapshot_dt]_pdr.txt, and contains the + following columns: + - Node ID + - Node type + - Degree ratio + - Number of infected edges (always patient-related) + - Total number of patient-related edges + - Total number of edges (i.e. degree of node_x) + """ + if self.edges_infected == False: + logging.error('This operation requires infection data on edges !') + return None + exact_path = self.data_dir if export_path is None else export_path + logging.info('Calculating patient degree ratio...') + write_count = 0 + with open(os.path.join(exact_path, self.snapshot_dt.strftime('%Y_%m_%d') + '_pdr.txt'), 'w') as outfile: + outfile.write(f"Node ID{csv_sep}Node Type{csv_sep}Degree Ratio{csv_sep}Number of Infected Edges{csv_sep}" + f"Total Patient Edges{csv_sep}Total Edges\n") + for each_node in self.S_GRAPH.nodes(data=True): + # each_node will be a tuple of length 2 --> ( 'node_id', {'att_1' : 'att_value1', ... } ) + this_node_edges = self.S_GRAPH.edges(each_node[0], data=True, keys=True) + # will return a list of tuples + # --> [ ( 'node_id', 'target_id1', key, {attr_dict} ), ('node_id', 'target_id2', key, {attr_dict} ),...] + pat_edges = [edge for edge in this_node_edges if 'Patient' in edge[3]['type']] + # indicates a type of "Patient-XXX" node + infected_pat_edges = [True if edge[3]['infected'] is True else False for edge in pat_edges] + + # Write to output file: + outfile.write(f"{each_node[0]}{csv_sep}{each_node[1]['type']}{csv_sep}" + f"{sum(infected_pat_edges)/len(infected_pat_edges)}{csv_sep}" + f"{len([entry for entry in infected_pat_edges if entry == True])}{csv_sep}" + f"{len(pat_edges)}{csv_sep}{len(this_node_edges)}\n") + write_count += 1 + # Write to log + logging.info(f"Successfully wrote patient degree ratios for {write_count} nodes to " + f"{os.path.join(exact_path, self.snapshot_dt.strftime('%Y_%m_%d') + '_pdr.txt')}") + + def export_total_degree_ratio(self, csv_sep=';', export_path=None): + """Exports total degree ratio (TDR) for all nodes in the network. + + Will calculate and export the total degree ratio for all nodes in the network, which is defined for a + single *node_x* as: + + :math:`TDR = \\frac{Number~of~infected~edges~between~node + \\_x~and~patients}{Total~number~of~edges~leading~to~node\\_x}` + + The result file will be written to a file [self.data_dir]/[self.snapshot_dt]_tdr.txt, and contains the + following columns: + + - Node ID + - Node type + - Degree ratio + - Number of infected edges (always patient-related) + - Total number of edges for node_x (also includes non-patient-related edges) + + Args: + csv_sep (str): Separator for created csv file. + export_path (str): Path to which node files will be written. If set to `None` (the default), the + exported file will be written to `self.data_dir`. + """ + exact_path = self.data_dir if export_path is None else export_path + logging.info('Calculating total degree ratio...') + write_count = 0 + with open(os.path.join(exact_path, self.snapshot_dt.strftime('%Y_%m_%d') + '_tdr.txt'), 'w') as outfile: + outfile.write(f"Node ID{csv_sep}Node Type{csv_sep}Degree Ratio{csv_sep}" + f"Number of Infected Edges{csv_sep}Total Edges\n") + for each_node in self.S_GRAPH.nodes(data=True): + # each_node will be a tuple of length 2 --> ( 'node_id', {'att_1' : 'att_value1', ... } ) + this_node_edges = self.S_GRAPH.edges(each_node[0], data=True, keys=True) + # will return a list of tuples + # --> [ ( 'node_id', 'target_id1', key, {attr_dict} ), ('node_id', 'target_id2', key, {attr_dict} ),...] + infected_edges = [True if edge[3]['infected'] == True else False for edge in this_node_edges] + # Write to output file: + outfile.write(f"{each_node[0]}{csv_sep}{each_node[1]['type']}{csv_sep}" + f"{sum(infected_edges)/len(this_node_edges)}{csv_sep}" + f"{len([entry for entry in infected_edges if entry == True])}{csv_sep}" + f"{len(this_node_edges)}\n") + write_count += 1 + # Write to log + logging.info(f"Successfully wrote total degree ratios for {write_count} nodes to " + f"{os.path.join(exact_path, self.snapshot_dt.strftime('%Y_%m_%d') + '_tdr.txt')}") + + def export_shortest_path_length_overview(self, focus_nodes=None, csv_sep=';', export_path=None): + """Exports an overview of shortest path lengths in the network to self.data_dir. + + Exports an overview of shortest path lengths of all nodes in the network (if *focus_nodes* is `None`, + default). If a list of node identifiers is provided in *focus_nodes* instead, only these nodes will be + considered for the data export. Data are exported to the self.data_dir directory. + + Note: + This overview only considers *one* possible shortest path between any given pair of nodes, not all paths. + + Args: + focus_nodes (list or None): List of nodes considered for distribution. + csv_sep (str): Separator used in export file. + export_path (str): Path to which node files will be written. If set to `None` (the default), the + exported file will be written to `self.data_dir`. + + """ + exact_path = self.data_dir if export_path is None else export_path + target_nodes = focus_nodes if focus_nodes is not None else self.S_GRAPH.nodes + node_combinations = list(itertools.combinations(target_nodes, 2)) + logging.info(f'Exporting shortest path length overview for {len(node_combinations)} node combinations...') + shortest_path_lengths = [] # list holding all encountered shortest path lengths + for count, combo_tuple in enumerate(node_combinations): + if count % 1000 == 0: + logging.info(f" <> Processed {count} combinations") + if nx.has_path(self.S_GRAPH, combo_tuple[0], combo_tuple[1]) is False: + continue # indicates a node pair in disconnected parts of network + shortest_path_lengths.append(len(nx.shortest_path(self.S_GRAPH, source=combo_tuple[0], + target=combo_tuple[1]))) + # Then export results to file + node_counts = Counter(shortest_path_lengths) + with open(os.path.join(exact_path, self.snapshot_dt.strftime('%Y_%m_%d') + '_plov.txt'), 'w') as outfile: + outfile.write(f"Path Length{csv_sep}Count\n") + for each_key in sorted(node_counts.keys()): + outfile.write(f"{each_key}{csv_sep}{node_counts[each_key]}\n") + # Write to log and exit + logging.info(f'Successfully exported shortest path length overview to' + f"{os.path.join(exact_path, self.snapshot_dt.strftime('%Y_%m_%d') + '_plov.txt')}") + + def export_node_betweenness(self, csv_sep=';', export_path=None): + """Exports node betweenness. + + This function will export node betweenness for all nodes from the network. This is done by exporting the sum + of all fractions of shortest paths a particular node is in, which is found in the attribute keys starting with + the "SP-" prefix (e.g. "SP-Node1-Node4"). These tuples contain 2 entries, the first being the fraction of + shortest path this node is found in (between the given pair of nodes), and the second one being the total + number of shortest paths. This function can only be executed once the function update_shortest_path_statistics() + has been called. Nodes without an attribute key starting with the "SP-" prefix will have node betweenness of 0. + + The exported file is written into self.data_dir and contains 3 columns: + + - Node ID + - Node Type + - Betweenness Score + + Args: + csv_sep (str): separator to be used in export file. + export_path (str): Path to which node files will be written. If set to `None` (the default), the + exported file will be written to `self.data_dir`. + """ + if self.shortest_path_stats is False: + logging.error(f"This function requires specific node attributes which can be added with the function " + f"update_shortest_path_statitics()") + raise self.NodeBetweennessException('Missing a required call to update_shortest_path_statitics()') + exact_path = self.data_dir if export_path is None else export_path + with open(os.path.join(exact_path, self.snapshot_dt.strftime('%Y_%m_%d') + '_nbc.txt'), 'w') as outfile: + outfile.write(csv_sep.join(['Node ID', 'Node Type', 'Betweenness_Score']) + '\n') + write_counter = 0 + for node_tuple in self.S_GRAPH.nodes(data=True): + # Returns tuples of length 2 --> (node_id, attribute_dict) + write_string = [node_tuple[0], self.identify_id(node_tuple[0])] + target_keys = [each_key for each_key in node_tuple[1].keys() if each_key.startswith('SP-')] + betweenness_score = sum([node_tuple[1][each_key][0] / node_tuple[1][each_key][1] + for each_key in target_keys]) + write_string.append(str(betweenness_score)) + # Then write to outfile + outfile.write(csv_sep.join(write_string) + '\n') + write_counter += 1 + # Log progress + logging.info(f"Successfully wrote betweenness scores for {write_counter} nodes to " + f"{os.path.join(exact_path, self.snapshot_dt.strftime('%Y_%m_%d') + '_nbc.txt')}") + + def write_node_files(self, attribute_subset=None, export_path=None): + """Writes a JSON representation of all nodes in the network to file. + + For each node in the network, this function will write a JSON representation containing a dictionary of + node_data_dict[keys], where keys are all entries in node_attributes if that key is actually found in the node + data dictionary. The file is named [node_id].json and written to the self.data_dir directory. + + This function is mainly used to "hard-store" values for highly ressource intensive calculations such as + betweenness centrality and avoid memory overflows. + + Note: + This function will also set the self.node_files_written flag to ``True``. + + Args: + attribute_subset (list): List of keys in a node's attribute_dict to be included in the written JSON + representation of the node. If set to `None` (the default), all attributes will + be included. + export_path (str): Path to which node files will be written. If set to `None` (the default), all + files will be written to `self.data_dir`. + """ + exact_path = self.data_dir if export_path is None else export_path + logging.info(f"Writing node files to {os.path.abspath(exact_path)}...") + write_count = 0 + for node_tuple in self.S_GRAPH.nodes(data=True): + # Returns tuples of length 2 --> (node_id, attribute_dict) + if write_count % 5000 == 0: + logging.info(f"Wrote {write_count} nodes to file.") + if attribute_subset is None: + write_keys = [key for key in node_tuple[1]] + else: + write_keys = [key for key in node_tuple[1] if key in attribute_subset] + write_dict = {node_attr: node_tuple[1][node_attr] for node_attr in write_keys} + json.dump(write_dict, open(os.path.join(exact_path, self.parse_filename(node_tuple[0]) + '.json'), 'w')) + write_count += 1 + # Write to log and exit + logging.info(f"Successfully wrote all nodes to file!") + self.node_files_written = True + + + + + + + + + + + # logging.info(f"##################################################################################") + # logging.info(f'Calculating betweenness centrality (based on {"all" if considered_nodes is None else "specific"} nodes)...') + # if considered_nodes is None: + # bw_cent = nx.betweenness_centrality(self.S_GRAPH, normalized=normalize) + # logging.info('Successfully calculated betweenness centrality for all nodes.') + # return bw_cent + # else: + # node_combinations = list(itertools.combinations(considered_nodes, 2)) # Returns a list of tuples containing all unique pairs of nodes in considered_nodes + # logging.info(f'--> Calculating betweenness centrality considering {len(considered_nodes)} nodes yielding {len(node_combinations)} combinations') + # node_dict = {each_node[0] : {'scores' : [], 'attributes' : each_node[1] } # dictionary mapping node identifiers to lists of tuples of the format (shortest_paths_through_node, number_of_shortest_paths) for each combination of considered_nodes + # for each_node in self.S_GRAPH.nodes(data=True) } # S_GRAPH.nodes(data=True) returns tuples of length 2 of ('node_id', attribute_dict) + # for index, combo_tuple in enumerate(node_combinations): + # if index % 1 == 0: + # logging.info(f" <> Processed {index} combinations") + # all_shortest_paths = list(nx.all_shortest_paths(self.S_GRAPH, source=combo_tuple[0], target=combo_tuple[1])) # Yields a list of list of shortest paths for that combination of nodes + # all_shortest_paths = [path_list[1:len(path_list) - 1] for path_list in all_shortest_paths] # Remove the source and target node in each list of shortest path, as these should not be counted + # involved_nodes = set([node for sublist in all_shortest_paths for node in sublist]) # includes all involved nodes + # for inv_node in involved_nodes: + # involved_list = [True if inv_node in sub_list else False for sub_list in all_shortest_paths] # list containing True if inv_node is involved in a particular list of all_shortest_paths, and False otherwise + # node_dict[inv_node]['scores'].append((sum(involved_list), len(involved_list))) + # ### Then combine all entries in node_dict to true scores + # score_dict = {each_key : sum([each_tuple[0]/each_tuple[1] for each_tuple in node_dict[each_key]['scores']]) for each_key in node_dict.keys()} + # if normalize == True: + # total_nodes = len(self.S_GRAPH.nodes) + # score_dict = {each_key : score_dict[each_key] * ( 2 / ((total_nodes-1) * (total_nodes-2)) ) for each_key in score_dict.keys()} + # logging.info('Successfully calculated betweenness centrality.') + # self.betweenness_centrality = score_dict + # ### Write results to file + # with open(os.path.join(self.data_dir, self.snapshot_dt.strftime('%Y_%m_%d') + '_node_bwn.txt'), 'w') as outfile: + # outfile.write(f'Node ID{csv_sep}Node Type{csv_sep}Betweenness_Score\n') + # for key in sorted(score_dict.keys()): + # outfile.write(f"{key}{csv_sep}{node_dict[key]['attributes']['type']}{csv_sep}{score_dict[key]}\n") + # logging.info(f"Successfully wrote betweenness scores for {len(score_dict.keys())} nodes to {os.path.join(self.data_dir, self.snapshot_dt.strftime('%Y_%m_%d') + '_node_bwn.txt')}") + + + + # def add_patient_contacts(self, patient_contacts, patient_dict): + # """ + # Adds patient-patient interactions in cases where 2 patients came into contact with each other during their relevant case. + # + # :param patient_contacts: List containing tuples of length 6 of either the format: (source_pat_id, dest_pat_id, start_overlap_dt, end_overlap_dt, room_name, "kontakt_raum") + # or the format: (source_pat_id, dest_pat_id, start_overlap_dt, end_overlap_dt, ward_name, "kontakt_org") + # :param patient_dict: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} + # """ + # contact_count = 0 + # for contact in patient_contacts: # list of tuples --> can be directly added to the network + # if patient_dict[contact[0]].get_relevant_case() is not None and patient_dict[contact[1]].get_relevant_case() is not None: # make sure to include only patients with a relevant date ! + # self.S_GRAPH.add_node(str(contact[0]), type = 'Patient') + # self.S_GRAPH.add_node(str(contact[1]), type = 'Patient') + # self.S_GRAPH.add_edge(str(contact[0]), str(contact[1])) + # contact_count += 1 + # # Update log file + # logging.info(f'GRAPH: Created {contact_count} patient-patient contacts.') + + # def add_patient_room(self, patient_dict): + # """ + # Adds patient-room interactions, i.e. rooms occupied by a particular patient during his or her relevant case. + # + # :param patient_dict: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} + # """ + # relcase_count = 0 + # patient_count = 0 + # room_count = 0 + # + # for patient in patient_dict.values(): + # patient_count += 1 + # pat_rel_case = patient.get_relevant_case() + # if pat_rel_case is not None: + # relcase_count += 1 + # for move in pat_rel_case.moves.values(): # the Case().moves attribute is a dictionary mapping the order of moves to Moves() objects + # if move.room is not None: + # self.S_GRAPH.add_node(str(patient.patient_id), type = 'Patient') + # self.S_GRAPH.add_node(str(move.room.name), type='Room') + # self.S_GRAPH.add_edge(str(patient.patient_id), str(move.room.name)) + # room_count += 1 + # # update logfile + # logging.info(f'GRAPH: Wrote {room_count} rooms (based on moves) for {relcase_count} patients with relevant cases out of {patient_count} patients.') + # + # def add_patient_employee(self, patient_dict): + # """ + # Add all contacts between an employee and a patient during the patient's relevant case. + # + # :param patient_dict: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} + # """ + # pat_count = 0 + # relcase_count = 0 + # contact_count = 0 + # + # for k, p in patient_dict.items(): + # pat_rel_case = p.get_relevant_case() # returns None if patient has no relevant case + # pat_count += 1 + # if pat_rel_case is not None: + # relcase_count += 1 + # for t in pat_rel_case.appointments: + # for e in t.employees: + # if e.mitarbeiter_id != "-1": # indicates an unknown mitarbeiter - these cases are ignored + # self.S_GRAPH.add_node(str(p.patient_id), type = 'Patient') + # self.S_GRAPH.add_node(str(e.mitarbeiter_id), type = 'Employee') + # self.S_GRAPH.add_edge(str(p.patient_id), str(e.mitarbeiter_id)) + # contact_count += 1 + # # Update log file + # logging.info(f'GRAPH: Created {contact_count} contacts in {relcase_count} relevant cases from {pat_count} patients.') + + ################################################################################################################ + ### Functions for retrieving graph statistics + ################################################################################################################ + + # def get_node_count(self, type_filter = None): + # """ + # Return the number of nodes in the graph, possibly also applying a filter to the type of nodes to be returned. + # + # :param type_filter: Filter for the "type" attribute of nodes. If None, all nodes in the graph will be returned. + # :return: Number of nodes matching the specified filter criteria. + # """ + # if filter is None: + # return self.S_GRAPH.number_of_nodes() + # else: + # return len([data for node, data in self.S_GRAPH.nodes(data=True) if data['type'] == type_filter]) # S_GRAPH.nodes() returns a list of dictionaries of all node attributes + # + # def get_edge_count(self, type_filter = None): + # """ + # Return the number of edges in the graph, possibly also applying a filter to the type of edge to be returned. + # + # :param type_filter: Filter for the "type" attribute of edges. If None, all edges in the graph will be returned. + # :return: Number of edges matching the specified filter criteria. + # """ + # if type is None: + # return self.S_GRAPH.number_of_edges() + # else: + # return len([data_tuple for data_tuple in self.S_GRAPH.edges(data=True) if data_tuple[2]['type'] == type_filter]) # S_GRAPH.edges() returns a list of tuples of length 3 in the format (source_node, dest_node, {'attribute1' : value1, ...}) + # + # def get_node_degree_centrality(self): + # """ + # Calculate the node degree centrality for each node of the graph. + # + # :return: List of tuples of length 2 --> (node_name, centrality) + # """ + # return nx.degree_centrality(self.S_GRAPH) + # + # def get_node_betweenness_centrality(self, normalized = False): + # """ + # Calculate the node betweenness centrality (raw or normalized) for each node of the graph. + # + # :param normalized: boolean indicating whether or not to return raw (False) or normalized (True) values + # :return: List of tuples of length 2 --> (node_name, centrality) + # """ + # return nx.betweenness_centrality(self.S_GRAPH, normalized=normalized) + # [END OF FILE} diff --git a/spitalhygiene/vre/src/main/python/vre/preprocessor.py b/spitalhygiene/vre/src/main/python/vre/preprocessor.py new file mode 100644 index 0000000..1d37d12 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/preprocessor.py @@ -0,0 +1,200 @@ +# -*- coding: utf-8 -*- +"""This script contains various functions for pre-processing data required in the VRE project. +This includes: + +- Regenerating the ward screening overview data stored in the ``Atelier_DataScience.dbo.WARD_SCREENINGS`` table +- ... + +Please refer to the script code for details on the various functions. + +----- +""" + +import pyodbc +import os +import configparser +import datetime + +def ExecuteSQL(sql_command, connection_file, trusted_connection=True): + """ + Executes an arbitrary SQL command, but does **not** return any results. + + Args: + sql_command (str): SQL command to be executed + connection_file (str): path to file containing information used for server connection and authentication, + as well as database selection (read and passed to ``pyodbc.connect()`` ) + + trusted_connection (bool): additional argument passed to pyodbc.connect(), converted to "yes" if ``True`` and + "no" otherwise (defaults to ``True``) + """ + connection_string = ';'.join([line.replace('\n', '') for line in open(connection_file, 'r')]) + + conn = pyodbc.connect(connection_string, trusted_connection='yes' if trusted_connection else 'no') + cursor = conn.cursor() + + cursor.execute(sql_command) + + cursor.commit() # this is required for INSERT, DELETE and UPDATE statements to take effect in the DB + + cursor.close() + conn.close() + +def RecreateWardOverviewData(csv_sep=';'): + """ + Recreates dates at which specific screening types were active in various clinics. + + This information is found in the ``[...]/vre_input/screening_overview/screening_overview.csv`` file. Its contents + are used to create a query for updating the ``Atelier_DataScience.dbo.WARD_SCREENINGS`` table. This query is written + to the ``[...]/vre_output/manual_sql_queries`` folder and named ``update_ward_screenings.sql``, + since the Atelier_Datascience_Reader does not have permission to execute TRUNCATE, DELETE, INSERT or UPDATE + statements. + + To Do: + Find a solution to automate this part. + + Args: + csv_sep (str): separator used in file (defaults to ``;``) + """ + # Extract correct filepath + this_filepath = os.path.dirname(os.path.realpath(__file__)) + # contains the directory in which this script is located, irrespective of the current working directory + + # Load config file: + config_reader = configparser.ConfigParser() + config_reader.read(os.path.join(this_filepath, 'BasicConfig.ini')) + + # Prepare INSERT statement for table + sql_statement = 'TRUNCATE TABLE [Atelier_DataScience].[dbo].[WARD_SCREENINGS];\n' + + # Load data from file + all_screening_data = [each_line.replace('\n', '') for each_line in + open(os.path.join(config_reader['PATHS']['input_dir'], 'screening_overview', + 'screening_overview.csv'), 'r')] + # --> Note: the data file contains the date of active screening in column 1, day of week in column 2 (not used), + # and active screenings in all other "pflegerischen" wards in subsequent columns + + for index, line in enumerate(all_screening_data): + if index == 0: # first line contains headers + all_wards = line.split(csv_sep) + else: + line_data = line.split(csv_sep) + for line_index, each_entry in enumerate(line_data): + if line_index > 1 and each_entry != '': + sql_statement += f"INSERT INTO [Atelier_DataScience].[dbo].[WARD_SCREENINGS] VALUES" \ + f"( '{datetime.datetime.strptime(line_data[0], '%d.%m.%Y').strftime('%Y-%m-%d')}'," \ + f"'{all_wards[line_index]}', '{each_entry}' )\n" + # Write statement to file + with open(os.path.join(config_reader['PATHS']['output_dir'], 'manual_sql_queries', + 'update_ward_screenings.sql'), 'w') as writequery: + writequery.write(sql_statement) + +def RecreateHospitalMap(csv_sep=';'): + """ + Recreates the *Hospital Map*. + + This map links the following important entities in the model: + + - fachliche OE + - pflegerische OE + - official abbreviation of pflegerische OE + - building in which pflegerische OE is located + - floor of building in which pflegerische OE is located + + All information required is found in the ``[...]/vre_input/maps/insel_map.csv`` file. Its contents + are used to create a query for updating the ``Atelier_DataScience.dbo.INSEL_MAP`` table. This query is written + to the ``[...]/vre_output/manual_sql_queries`` folder and named ``update_insel_map.sql``, since the + Atelier_Datascience_Reader does not have permission to execute TRUNCATE, DELETE, INSERT or UPDATE statements. + + Note: + Floors are very important, since rooms are exported "floor-wise" from Waveware. + + Args: + csv_sep: separator used in the read file (defaults to ``;``) + """ + # Extract correct filepath + this_filepath = os.path.dirname(os.path.realpath(__file__)) + # contains the directory in which this script is located, irrespective of the current working directory + + # Load config file: + config_reader = configparser.ConfigParser() + config_reader.read(os.path.join(this_filepath, 'BasicConfig.ini')) + + # Prepare INSERT statement for table + sql_statement = 'TRUNCATE TABLE [Atelier_DataScience].[dbo].[INSEL_MAP];\n' + + # Load data from file + all_map_data = [each_line.replace('\n', '') for each_line in + open(os.path.join(config_reader['PATHS']['input_dir'], 'maps', 'insel_map.csv'), 'r')] + + for index, each_line in enumerate(all_map_data): + if index > 0: # skip header row + line_data = each_line.split(csv_sep) + sql_statement += f'INSERT INTO [Atelier_DataScience].[dbo].[INSEL_MAP] VALUES (' + sql_statement += f"'{line_data[0]}', " + sql_statement += f"'{line_data[1]}', " if line_data[1] != '' else 'NULL, ' + sql_statement += f"'{line_data[2]}', " if line_data[2] != '' else 'NULL, ' + sql_statement += f"'{line_data[3]}', " + sql_statement += f"'{line_data[4]}', " + sql_statement += f"'{line_data[5]}', " + sql_statement += f"'{line_data[6]}', " if line_data[6] != '' else 'NULL, ' + sql_statement += f"'{line_data[7]}', " + sql_statement += f"'{line_data[8]}', " if line_data[8] != '' else 'NULL, ' + sql_statement += f"'{line_data[9]}' )\n" + + # Write statement to file + with open(os.path.join(config_reader['PATHS']['output_dir'], 'manual_sql_queries', + 'update_insel_map.sql'), 'w') as writequery: + writequery.write(sql_statement) + +def RecreatePflegerischeOEMap(csv_sep=';'): + """ + Recreates the map for pflegerische OEs. + + This map links "free-text" pflegerische OE names to the *official* names in the ``OE_pflege_abk`` column of the + ``Atelier_DataScience.dbo.INSEL_MAP`` table. + + All information required is found in the ``[...]/vre_input/maps/oe_pflege_map.csv`` file. Its contents + are used to create a query for updating the ``Atelier_DataScience.dbo.OE_PFLEGE_MAP`` table. This query is written + to the ``[...]/vre_output/manual_sql_queries`` folder and named ``update_oe_pflege_map.sql``, since the + Atelier_Datascience_Reader does not have permission to execute TRUNCATE, DELETE, INSERT or UPDATE statements. + + Args: + csv_sep: separator used in the read file (defaults to ``;``) + """ + # Extract correct filepath + this_filepath = os.path.dirname(os.path.realpath(__file__)) + # contains the directory in which this script is located, irrespective of the current working directory + + # Load config file: + config_reader = configparser.ConfigParser() + config_reader.read(os.path.join(this_filepath, 'BasicConfig.ini')) + + # Prepare INSERT statement for table + sql_statement = 'TRUNCATE TABLE [Atelier_DataScience].[dbo].[OE_PFLEGE_MAP];\n' + + # Load data from file + all_map_data = [each_line.replace('\n', '') for each_line in + open(os.path.join(config_reader['PATHS']['input_dir'], 'maps', 'oe_pflege_map.csv'), 'r')] + + for index, each_line in enumerate(all_map_data): + if index > 0: # skip header row + line_data = each_line.split(csv_sep) + sql_statement += f'INSERT INTO [Atelier_DataScience].[dbo].[OE_PFLEGE_MAP] VALUES (' + sql_statement += f"'{line_data[0]}', '{line_data[1]}' )\n" + + # Write statement to file + with open(os.path.join(config_reader['PATHS']['output_dir'], 'manual_sql_queries', + 'update_oe_pflege_map.sql'), 'w') as writequery: + writequery.write(sql_statement) + + +def RecreateScreeningData(csv_sep=';'): + """ + + :param csv_sep: + :return: + """ + + + + diff --git a/spitalhygiene/vre/src/main/python/vre/quality_control/__init__.py b/spitalhygiene/vre/src/main/python/vre/quality_control/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/spitalhygiene/vre/src/main/python/vre/quality_control/data_purification.py b/spitalhygiene/vre/src/main/python/vre/quality_control/data_purification.py new file mode 100644 index 0000000..1485081 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/quality_control/data_purification.py @@ -0,0 +1,64 @@ +import os + +################################################################################################################ +### Contains important functions for writing data used in the VRE model to file, so that the underlying data +### can be inspected and potentially purified manually +################################################################################################################ +### define functions for writing to various files + +this_filepath = os.path.dirname(os.path.realpath(__file__)) +csv_sep = ';' + +def write_geraet(geraet_tuple, filepath = os.path.join(this_filepath, 'Geraet.csv'), csv_sep = csv_sep): + """ + Will write the two entries in geraet_tuple (tuple of length 2) to filepath using csv_sep. + :param geraet: Tuple of length 2 + :param filepath: Path of the CSV file to write to in "a" mode + :param csv_sep: CSV separator to use in the file + """ + try: + with open(filepath, 'a') as writefile: + writefile.write(f"{geraet_tuple[0]}{csv_sep}{geraet_tuple[1]}\n") + except Exception as e: + print(f"Error: {e}") + +def write_patient_case(case, has_risk, filepath = os.path.join(this_filepath, 'Pat_Case.csv'), csv_sep = csv_sep): + """ + Will write the relevant case and associated patient ID (each case also has an associated patient ID) to filepath using csv_sep. + :param case: A Case() object + :param has_risk: Boolean indicating whether or not the patient had a risk during his or her relevant stay (i.e. was ever in contact with any VRE screening) + :param filepath: Path of the CSV file to write to in "a" mode + :param csv_sep: CSV separator to use in the file + """ + try: + with open(filepath, 'a') as writefile: + writefile.write(f"{case.patient_id}{csv_sep}{case.case_id}{csv_sep}{has_risk}\n") + except Exception as e: + print(f"Error: {e}") + +def write_employee(employee_id, filepath = os.path.join(this_filepath, 'Employee.csv')): + """ + Will write employee_id to filepath using csv_sep. + :param employee: string + :param filepath: Path of the CSV file to write to in "a" mode + """ + try: + with open(filepath, 'a') as writefile: + writefile.write(f"{employee_id}\n") + except Exception as e: + print(f"Error: {e}") + +def write_room(room, filepath = os.path.join(this_filepath, 'Room.csv') ): + """ + Will write room to filepath using csv_sep. + :param room: string + :param filepath: Path of the CSV file to write to in "a" mode + """ + try: + with open(filepath, 'a') as writefile: + writefile.write(f"{room}\n") + except Exception as e: + print(f"Error: {e}") + + + diff --git a/spitalhygiene/vre/src/main/python/vre/quality_control/distribution_export.py b/spitalhygiene/vre/src/main/python/vre/quality_control/distribution_export.py new file mode 100644 index 0000000..393e072 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/quality_control/distribution_export.py @@ -0,0 +1,133 @@ + + +################################################################################################################ +### Contains important functions for exporting count distributions (i.e. appearances) of various +### entities in the VRE model +################################################################################################################ + +def get_room_distribution(appointment_dict, file_path, write_mode = 'w', csv_sep = ';'): + """ + This function will count the occurrence of all Room() objects based on the data contained in all Appointments() of the VRE model. This information + is extracted from the Appointment().rooms attribute. The written file contains: + --> Room names + --> Occurrence counts + + :param appointment_dict: A dictionary mapping appointment.termin_id to corresponding Appointment() objects + :param file_path: Path for writing to file + :param write_mode: Write mode for file_path (default is 'w') + :param csv_sep: Separator for CSV files (default is ';') + """ + room_counts = {} # dictionary containing room counts + for apmnt in appointment_dict.values(): + for room_obj in apmnt.rooms: + if room_obj.name not in room_counts.keys(): + room_counts[room_obj.name] = 1 + else: # increment occurrence of room + room_counts[room_obj.name] += 1 + ### Then write results to file + room_names = sorted(list(room_counts.keys())) + with open(file_path, write_mode) as writefile: + writefile.write(f"Room_Name{csv_sep}Count\n") + for each_name in room_names: + writefile.write(f"{each_name}{csv_sep}{room_counts[each_name]}" + '\n') + print('\nSuccessfully wrote room counts to file !\n') + +def get_ward_distribution(case_dict, file_path, write_mode = 'w', csv_sep = ';'): + """ + This function will count the occurrence of all Ward() objects in the data. + This information is extracted from the Case().moves attribute, since each Move() object has an attribute Move().ward . The written file contains + --> Ward names + --> Occurrence counts + + :param case_dict: A dictionary mapping Case().case_id to corresponding Case() objects + :param file_path: Path for written file + :param write_mode: Write mode for file_path (default is 'w') + :param csv_sep: Separator for CSV files (default is ';') + """ + ward_count = {} + for each_case in case_dict.values(): + for each_move in each_case.moves.values(): + if each_move.ward is None: + continue + if each_move.ward.name not in ward_count.keys(): + ward_count[each_move.ward.name] = 1 + else: + ward_count[each_move.ward.name] += 1 + ### Write results to file + ward_names = sorted(list(ward_count.keys())) + with open(file_path, write_mode) as writefile: + writefile.write(f"Ward_Name{csv_sep}Count\n") + for each_name in ward_names: + writefile.write(f"{each_name}{csv_sep}{ward_count[each_name]}" + '\n') + print('\nSuccessfully wrote ward counts to file !\n') + +def collect_patient_info(pat_dict, file_path, write_mode = 'w', csv_sep = ';'): + """ + This function will collect important information on all patients in the VRE dataset. This information will be taken from various sources, most + importantly the Patient().cases attribute list. The information collected (and printed to file) includes: + --> Patient ID + --> Number of cases + --> Number of appointments + --> Total duration of all appointments + --> number of moves + --> Number of surgeries + --> Number of medications + + :param pat_dict: Dictionary mapping Patient().patient_id to corresponding Patient() objects + :param file_path: Path for written file + :param write_mode: Write mode for file_path (default is 'w') + :param csv_sep: Separator for CSV files (default is ';') + """ + pat_counts = {} # dictionary mapping patient ids to the various measures described in the docstring + for pat_tuple in pat_dict.items(): + if pat_tuple[0] not in pat_counts.keys(): + pat_counts[pat_tuple[0]] = {} + ### Extract number of cases + pat_counts[pat_tuple[0]]['Case Count'] = len(list(pat_tuple[1].cases)) + ### Extract other required metrics + apmnt_count = 0 + apmnt_duration = 0 + move_count = 0 + surgery_count = 0 + medication_count = 0 + for each_case in pat_tuple[1].cases: + apmnt_count += len(each_case.appointments) + for each_apmnt in each_case.appointments: + apmnt_duration += each_apmnt.dauer_in_min + move_count += len(list(each_case.moves.keys())) + surgery_count += len(each_case.surgeries) + medication_count += len(each_case.medications) + ### Add all extracted measures to pat_counts + pat_counts[pat_tuple[0]]['Apmnt Count'] = apmnt_count + pat_counts[pat_tuple[0]]['Apmnt Duration'] = apmnt_duration + pat_counts[pat_tuple[0]]['Move Count'] = move_count + pat_counts[pat_tuple[0]]['Surgery Count'] = surgery_count + pat_counts[pat_tuple[0]]['Medication Count'] = medication_count + ### Then write results to file + with open(file_path, write_mode) as write_file: + write_file.write(f"Patient_ID{csv_sep}Case_Count{csv_sep}Appointment_Count{csv_sep}Appointment_Duration{csv_sep}Move_Count{csv_sep}Surgery_Count{csv_sep}Medication_Count\n") + for each_tuple in pat_counts.items(): + data_list = [each_tuple[0]] + data_list.append(each_tuple[1]['Case Count']) + data_list.append(each_tuple[1]['Apmnt Count']) + data_list.append(each_tuple[1]['Apmnt Duration']) + data_list.append(each_tuple[1]['Move Count']) + data_list.append(each_tuple[1]['Surgery Count']) + data_list.append(each_tuple[1]['Medication Count']) + write_file.write(csv_sep.join(data_list) + '\n') + print('\nSuccessfully collected patient information !\n') + + + + + + + + + + + + + + + diff --git a/spitalhygiene/vre/src/main/python/vre/test.py b/spitalhygiene/vre/src/main/python/vre/test.py new file mode 100644 index 0000000..79b0fd6 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/test.py @@ -0,0 +1,18 @@ +from collections import Counter +# import networkx as nx +import datetime +import itertools +import random +import json + +# print(0 % 4) +# print(1 % 4) +# +# print(6 % 5) + + +# print(datetime.datetime.now() > datetime.date(2017, 1, 1)) + + + + diff --git a/spitalhygiene/vre/src/main/python/vre/test2.py b/spitalhygiene/vre/src/main/python/vre/test2.py new file mode 100644 index 0000000..5338b91 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/test2.py @@ -0,0 +1,160 @@ +import calendar +import networkx as nx +import logging +import itertools +from collections import Counter +import os + + +def update_shortest_path_statistics(graph, focus_nodes=None): + """ Prerequisite function for calculating betweenness centrality. + + Adds new attributes to all nodes in focus_nodes, where each attribute + is a pair of nodes (sorted alphabetically) with a "SP-" prefix to tuples + of length 2 containing + (shortest_paths_through_this_node, total_shortest_paths) + For example: + { 'SP-Node1-Node2': (2, 5) , 'SP-Node1-Node3': ... } } + + Note that this may result in a lot of additional attributes for nodes + which are integral to the network. This approach is chosen because of + the fact that the networkx module does not allow updates to a + dict-of-a-dict type of attributes (i.e. if these attributes were to + be combined in a 1st-level key 'shortest-paths', the entire content + would have to be copied every time a new node-pair attribute is added, + which would make the function extremely inefficient. + + This is an important prerequisite function for the calculation of + betweenness centrality. + + Args: + focus_nodes (list): list of node IDs. If set to None (the default), + all nodes in the network will be considered. + WARNING: this may be ressource-intensive ! + """ + target_nodes = focus_nodes if focus_nodes is not None else graph.nodes + node_combinations = list(itertools.combinations(target_nodes, 2)) + # Returns a list of tuples containing all unique pairs of nodes + # in considered_nodes + + logging.info(f'--> Adding shortest path statistics considering ' + f'{len(target_nodes)} nodes yielding ' + f'{len(node_combinations)} combinations') + for count, combo_tuple in enumerate(node_combinations): + if count % 100 == 0: + logging.info(f" <> Processed {count} combinations") + all_shortest_paths = list(nx.all_shortest_paths(graph, + source=combo_tuple[0], + target=combo_tuple[1])) # Yields an iterator of lists of shortest paths for the given combination of nodes + involved_nodes = [node for sublist in all_shortest_paths + for node in + sublist] # Contains all nodes involved in all combinations of shortest paths for the given combination of nodes + node_counts = Counter(involved_nodes) + sorted_pair = sorted([combo_tuple[0], combo_tuple[1]]) + update_attr_dict = { + each_key: {'SP-' + sorted_pair[0] + '-' + sorted_pair[1]: + (node_counts[each_key], len(all_shortest_paths))} + for each_key in node_counts + if each_key not in sorted_pair} + # Then update all involved nodes in the network + nx.set_node_attributes(graph, update_attr_dict) + # Write it all to log + logging.info(f"Successfully added betweenness statistics to the network !") + + +def export_node_betweenness(graph, data_dir, csv_sep=';'): + """ Function to export node betweenness. + + This function will export node betweenness for all nodes from the + network. This is done by exporting the sum of all fractions of shortest + paths a particular node is in, which is found in the attribute keys + starting with the "SP-" prefix (e.g. "SP-Node1-Node4"). These tuples + contain 2 entries, the first being the fraction of shortest path this + node is found in (between the given pair of nodes), and the second one + being the total number of shortest paths. This function can only be + executed once the function update_shortest_path_statistics() has been + called. Nodes without an attribute key starting with the "SP-" prefix + will have node betweenness of 0. + + The exported file is written into self.data_dir and contains 3 columns: + - Node ID + - Node Type + - Betweenness Score + + Args: + csv_sep (str): separator to be used in export file. + """ + # if self.shortest_path_stats is False: + # logging.error(f"This function requires specific node attributes " + # f"which can be added with the function " + # f"update_shortest_path_statitics()") + # raise self.NodeBetweennessException('Missing a required call to' + # 'update_shortest_path_statitics()') + with open(os.path.join(data_dir, + '_node_bwn.txt'), 'w') as outfile: + outfile.write(csv_sep.join(['Node ID', 'Node Type', + 'Betweenness_Score']) + '\n') + write_counter = 0 + for node_tuple in graph.nodes(data=True): + # Returns tuples of length 2 --> (node_id, attribute_dict) + write_string = [node_tuple[0], 'hello'] + target_keys = [each_key for each_key in node_tuple[1].keys() + if each_key.startswith('SP-')] + print(target_keys) + betweenness_score = sum([node_tuple[1][each_key][0] / node_tuple[1][each_key][1] + for each_key in target_keys]) + write_string.append(str(betweenness_score)) + # Then write to outfile + outfile.write(csv_sep.join(write_string) + '\n') + write_counter += 1 + # Log progress + logging.info(f"Successfully wrote betweenness scores for " + f"{write_counter} nodes") +####################################### + +gra = nx.MultiGraph() + + +gra.add_edge('B', 'A') +gra.add_edge('F', 'A') +gra.add_edge('E', 'A') +gra.add_edge('D', 'A') + +gra.add_edge('F', 'C') +gra.add_edge('B', 'C') +gra.add_edge('D', 'B') +gra.add_edge('G', 'J') + + +print(nx.shortest_path(gra, 'B', 'F')) +print(nx.shortest_path(gra, 'B', 'F')) +print(nx.shortest_path(gra, 'B', 'F')) +print(nx.shortest_path(gra, 'B', 'F')) + +update_shortest_path_statistics(gra, focus_nodes=['B', 'C', 'E']) + +export_node_betweenness(gra, data_dir='.') + +print([node for node in gra.nodes(data=True)]) + +sys.exit() + +# gra.add_edge('P1', 'X') + +# print(nx.shortest_path(gra, 'P1', 'P3')) +# +# print(nx.is_connected(gra)) +# +# sys.exit() + +gra.add_edge('E', 'P2') +gra.add_edge('P1', 'P2') + +print(calculate_node_betweenness(gra)) +print(calculate_node_betweenness(gra, considered_nodes=['P1', 'P3'])) + + +print(calendar.monthrange(2018, 1)) + + +print('hello') if True == True else '' From ef7968ee5973ccff7932e4895cafb715302a9d1e Mon Sep 17 00:00:00 2001 From: Zahnd Date: Mon, 29 Jul 2019 10:14:47 +0200 Subject: [PATCH 2/9] Added README.md and upddated .gitignore file --- .gitignore | 4 +- README.md | 5 + .../build/doctrees/Patient_Test_Data.doctree | Bin 0 -> 55990 bytes .../Docs/build/doctrees/VRE_Model.doctree | Bin 0 -> 3569 bytes .../Docs/build/doctrees/environment.pickle | Bin 0 -> 25249 bytes .../Docs/build/doctrees/index.doctree | Bin 0 -> 4924 bytes .../Docs/build/doctrees/resources.doctree | Bin 0 -> 16683 bytes 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mode 100644 spitalhygiene/Docs/build/html/searchindex.js create mode 100644 spitalhygiene/Docs/build/html/sql.html create mode 100644 spitalhygiene/Docs/build/html/vre.html diff --git a/.gitignore b/.gitignore index 128abd9..f49eb24 100644 --- a/.gitignore +++ b/.gitignore @@ -7,7 +7,7 @@ # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 # Ignore .idea directory -/spitalhygiene/.idea/* +spitalhygiene/.idea/* # User-specific stuff @@ -174,7 +174,6 @@ __pycache__/ # Distribution / packaging .Python -build/ develop-eggs/ dist/ downloads/ @@ -231,7 +230,6 @@ instance/ .scrapy # Sphinx documentation -docs/_build/ # PyBuilder target/ diff --git a/README.md b/README.md new file mode 100644 index 0000000..c613a85 --- /dev/null +++ b/README.md @@ -0,0 +1,5 @@ +# VRE Project Overview + +This project was originally maintained as a branch in Sqooba's `Insel` mono-repo, and therefore "packaged" into a sub-folders +named `spitalhygiene`. + diff --git 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  • resources folder
  • sql folder
    • +
  • Unused folder
  • vre/src/main/python/vre folder
    • +
  • vre/src/main/python/vre/model folder
  • Patient Test Data
    • @@ -157,15 +159,51 @@

    Python Module Index

    + a | + b | + c | d | + e | f | h | + i | + m | n | + p | q
    + + + + + + + + + + + + + + + + + + @@ -173,6 +211,19 @@

    Python Module Index

    + + + + + + + + @@ -190,6 +241,22 @@

    Python Module Index

    HDFS_data_loader + + + + + + + + + + @@ -198,6 +265,14 @@

    Python Module Index

    networkx_graph + + + + + diff --git a/spitalhygiene/Docs/build/html/resources.html b/spitalhygiene/Docs/build/html/resources.html index 88c0ee3..53a24ff 100644 --- a/spitalhygiene/Docs/build/html/resources.html +++ b/spitalhygiene/Docs/build/html/resources.html @@ -86,9 +86,11 @@
  • VRE Model Overview
  • resources folder
  • sql folder
    • +
  • Unused folder
  • vre/src/main/python/vre folder
  • Patient Test Data
    • @@ -191,7 +193,128 @@

    resources

  • trusted_connection (bool) – additional argument passed to pyodbc.connect(), converted to “yes” if True and -“no” otherwise

    • +“no” otherwise (defaults to True)

    + + + + + + +
    +

    File: preprocesor.py

    +

    This script contains various functions for pre-processing data required in the VRE project. +This includes:

    +
      +

    • Regenerating the ward screening overview data stored in the Atelier_DataScience.dbo.WARD_SCREENINGS table

      • +

    • …

      • +
    +

    Please refer to the script code for details on the various functions.

    +
    +
    +
    +preprocessor.ExecuteSQL(sql_command, connection_file, trusted_connection=True)
    +

    Executes an arbitrary SQL command, but does not return any results.

    +
    +
    Parameters
    +
      +

    • sql_command (str) – SQL command to be executed

      • +

    • connection_file (str) – path to file containing information used for server connection and authentication, +as well as database selection (read and passed to pyodbc.connect() )

      • +

    • trusted_connection (bool) – additional argument passed to pyodbc.connect(), converted to “yes” if True and +“no” otherwise (defaults to True)

      • +
    +
    +
    +
    + +
    +
    +preprocessor.RecreateHospitalMap(path_to_config_file, csv_sep=';')
    +

    Recreates the Hospital Map in the Atelier_DataScience.

    +

    This map links the following important entities in the model:

    +
      +

    • fachliche OE

      • +

    • pflegerische OE

      • +

    • official abbreviation of pflegerische OE

      • +

    • building in which pflegerische OE is located

      • +

    • floor of building in which pflegerische OE is located

      • +
    +

    All information required is found in the [...]/vre_input/maps/insel_map.csv file. Its contents +are used to create a query for updating the Atelier_DataScience.dbo.INSEL_MAP table. This query is written +to the [...]/vre_output/manual_sql_queries folder and named update_insel_map.sql, since the +Atelier_Datascience_Reader does not have permission to execute TRUNCATE, DELETE, INSERT or UPDATE statements.

    +
    +

    Note

    +

    Floors are very important, since rooms are exported “floor-wise” from Waveware.

    +
    +
    +
    Parameters
    +
      +

    • path_to_config_file (str) – path to the BasicConfig.ini file

      • +

    • csv_sep (str) – separator used in the read file (defaults to ;)

      • +
    +
    +
    +
    + +
    +
    +preprocessor.RecreatePflegerischeOEMap(path_to_config_file, csv_sep=';')
    +

    Recreates the map for pflegerische OEs in the Atelier_DataScience.

    +
    +
    This map links “free-text” pflegerische OE names to the official names in the OE_pflege_abk column of the

    Atelier_DataScience.dbo.INSEL_MAP table.

    +
    +
    +

    All information required is found in the [...]/vre_input/maps/oe_pflege_map.csv file. Its contents +are used to create a query for updating the Atelier_DataScience.dbo.OE_PFLEGE_MAP table. This query is written +to the [...]/vre_output/manual_sql_queries folder and named update_oe_pflege_map.sql, since the +Atelier_Datascience_Reader does not have permission to execute TRUNCATE, DELETE, INSERT or UPDATE statements.

    +
    +
    Parameters
    +
      +

    • path_to_config_file (str) – path to the BasicConfig.ini file

      • +

    • csv_sep (str) – separator used in the read file (defaults to ;)

      • +
    +
    +
    +
    + +
    +
    +preprocessor.RecreateScreeningData(path_to_config_file, csv_sep=';')
    +

    Recreates all screening data in the Atelier_DataScience.

    +

    All information required is found in the [...]/vre_input/screening_data/vre_screenings.csv file. Its contents +are used to create a query for updating the Atelier_DataScience.dbo.VRE_SCREENING_DATA table. This query is +written to the [...]/vre_output/manual_sql_queries folder and named update_VRE_SCREENING_DATA.sql, since the +Atelier_Datascience_Reader does not have permission to execute TRUNCATE, DELETE, INSERT or UPDATE statements.

    +
    +
    Parameters
    +
      +

    • path_to_config_file (str) – path to the BasicConfig.ini file

      • +

    • csv_sep (str) – separator used in the read file (defaults to ;)

      • +
    +
    +
    +
    + +
    +
    +preprocessor.RecreateWardOverviewData(path_to_config_file, csv_sep=';')
    +

    Recreates dates at which specific screening types were active in various clinics.

    +

    This information is found in the [...]/vre_input/screening_data/screening_overview.csv file. Its contents +are used to create a query for updating the Atelier_DataScience.dbo.WARD_SCREENINGS table. This query is written +to the [...]/vre_output/manual_sql_queries folder and named update_ward_screenings.sql, +since the Atelier_Datascience_Reader does not have permission to execute TRUNCATE, DELETE, INSERT or UPDATE +statements.

    +
    +
    To Do:

    Find a solution to automate this part.

    +
    +
    +
    +
    Parameters
    +
      +

    • path_to_config_file (str) – path to the BasicConfig.ini file

      • +

    • csv_sep (str) – separator used in the read file (defaults to ;)

    diff --git a/spitalhygiene/Docs/build/html/search.html b/spitalhygiene/Docs/build/html/search.html index ab2cd78..2ae886d 100644 --- a/spitalhygiene/Docs/build/html/search.html +++ b/spitalhygiene/Docs/build/html/search.html @@ -85,7 +85,9 @@
  • VRE Model Overview
  • resources folder
  • sql folder
    • +
  • Unused folder
  • vre/src/main/python/vre folder
    • +
  • vre/src/main/python/vre/model folder
  • Patient Test Data
    • diff --git a/spitalhygiene/Docs/build/html/searchindex.js b/spitalhygiene/Docs/build/html/searchindex.js index b036c79..5b1b430 100644 --- a/spitalhygiene/Docs/build/html/searchindex.js +++ b/spitalhygiene/Docs/build/html/searchindex.js @@ -1 +1 @@ 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ight:7,welcom:2,well:5,were:[5,7],what:7,when:7,where:[0,7],whether:[4,7],which:[4,5,7],wise:5,without:7,work:7,would:7,write:[5,7],write_node_fil:7,writesqltocsv:5,written:[5,7],x33671:4,year:[4,7],yes:5,z00:0,z01:0,z12:4,z34:0,z36:0,z39:[0,4],z50:0,z51:0,z54:0,z62:4,z88:0,z89:[0,4],z94:0,z99:0},titles:["Patient Test Data","Unused folder","VRE Model Overview","Welcome to the VRE Model documentation!","vre/src/main/python/vre/model folder","resources folder","sql folder","vre/src/main/python/vre folder"],titleterms:{appoint:4,bed:4,care:4,chop:4,code:[],data:0,data_compil:7,detail:[],devic:4,document:3,employe:4,feature_extractor:7,file:[4,5,7],folder:[1,4,5,6,7],hdfs_data_load:7,icd:4,indic:[],main:[4,7],medic:4,model:[2,3,4],networkx_graph:7,overview:2,patient:0,pleas:[],preprocesor:5,python:[4,7],query_atelier_data:5,refer:[],resourc:5,script:[],sql:6,src:[4,7],tabl:[],test:0,unus:1,vre:[2,3,4,7],welcom:3}}) \ No newline at end of file diff --git a/spitalhygiene/Docs/build/html/vre.html b/spitalhygiene/Docs/build/html/vre.html index b44956a..3dc6fc6 100644 --- a/spitalhygiene/Docs/build/html/vre.html +++ b/spitalhygiene/Docs/build/html/vre.html @@ -36,8 +36,8 @@ - - + + @@ -86,6 +86,7 @@
  • VRE Model Overview
  • resources folder
  • sql folder
    • +
  • Unused folder
  • vre/src/main/python/vre folder
    • +
  • vre/src/main/python/vre/model folder
  • Patient Test Data
    • @@ -372,7 +374,8 @@

    vre/src/main/py

    { “rooms” \(\longrightarrow\) Rooms,

    ”wards” \(\longrightarrow\) Wards, etc. }

    -

    Please refer to the vre/src/main/python/vre/model folder documentation for more details.

    +

    Please refer to the vre/src/main/python/vre/model folder documentation for more details on the +various objects.

    Return type
    @@ -493,8 +496,8 @@

    vre/src/main/py

  • The graph is undirected

    • -

  • The python built-in None-type object should not be used for attributes according to the networkx docs. Instead, -unknown values will be set to “NULL” or “”

    • +

  • The python built-in None-type object should not be used for attributes according to the networkx docs. +Instead, unknown values will be set to “NULL” or “”

    • @@ -513,7 +516,7 @@

    vre/src/main/py
    -add_network_data(patient_dict, subset='relevant_case', snapshot=datetime.datetime(2019, 6, 17, 12, 42, 25, 728731))
    +add_network_data(patient_dict, subset='relevant_case', snapshot=datetime.datetime(2019, 7, 31, 18, 21, 24, 991461))

    Adds nodes and edges data to the network.

    Nodes and edges are added based on the data in patient_dict according to the subset specified (see description of parameters below).

    @@ -545,7 +548,7 @@

    vre/src/main/py
    -export_node_betweenness(csv_sep=';')
    +export_node_betweenness(csv_sep=';', export_path=None)

    Exports node betweenness.

    This function will export node betweenness for all nodes from the network. This is done by exporting the sum of all fractions of shortest paths a particular node is in, which is found in the attribute keys starting with @@ -561,20 +564,28 @@

    vre/src/main/py
    Parameters
    -

    csv_sep (str) – separator to be used in export file.

    +
      +

    • csv_sep (str) – separator to be used in export file.

      • +

    • export_path (str) – Path to which node files will be written. If set to None (the default), the +exported file will be written to self.data_dir.

      • +

    -export_patient_degree_ratio(csv_sep=';')
    +export_patient_degree_ratio(csv_sep=';', export_path=None)

    Calculates and exports patient degree ratio for all nodes in the network.

    The patient degree ratio is defined for a single node_x as:

    number of infected edges between node_x and patients / number of total edges between node_x and patients

    Parameters
    -

    csv_sep (str) – Separator for created csv file.

    +
      +

    • csv_sep (str) – Separator for created csv file.

      • +

    • export_path (str) – Path to which export file will be written. If set to None (the default), the +exported file will be written to self.data_dir.

      • +

    The result file will be written to the path [self.data_dir]/[self.snapshot_dt]_pdr.txt, and contains the @@ -589,7 +600,7 @@

    vre/src/main/py
    -export_shortest_path_length_overview(focus_nodes=None, csv_sep=';')
    +export_shortest_path_length_overview(focus_nodes=None, csv_sep=';', export_path=None)

    Exports an overview of shortest path lengths in the network to self.data_dir.

    Exports an overview of shortest path lengths of all nodes in the network (if focus_nodes is None, default). If a list of node identifiers is provided in focus_nodes instead, only these nodes will be @@ -603,6 +614,8 @@

    vre/src/main/py

    • focus_nodes (list or None) – List of nodes considered for distribution.

    • csv_sep (str) – Separator used in export file.

      • +

    • export_path (str) – Path to which node files will be written. If set to None (the default), the +exported file will be written to self.data_dir.

    @@ -610,7 +623,7 @@

    vre/src/main/py
    -export_total_degree_ratio(csv_sep=';')
    +export_total_degree_ratio(csv_sep=';', export_path=None)

    Exports total degree ratio (TDR) for all nodes in the network.

    Will calculate and export the total degree ratio for all nodes in the network, which is defined for a single node_x as:

    @@ -627,7 +640,11 @@

    vre/src/main/py
    Parameters
    -

    csv_sep (str) – Separator for created csv file.

    +
      +

    • csv_sep (str) – Separator for created csv file.

      • +

    • export_path (str) – Path to which node files will be written. If set to None (the default), the +exported file will be written to self.data_dir.

      • +

    @@ -941,11 +958,14 @@

    vre/src/main/py with a SP- prefix to tuples of length 2 containing (shortest_paths_through_this_node, total_shortest_paths) For example:

    { 'SP-Node1-Node2': (2, 5) , 'SP-Node1-Node3': (1, 8), ... } }

    -

    Note that this may result in a lot of additional attributes for nodes which are integral to the network. This -approach is chosen because of the fact that the networkx module does not allow updates to a dict-of-a-dict type -of attributes (i.e. if these attributes were to be combined in a 1st-level key ‘shortest-paths’, the entire +

    +

    Note

    +

    This may result in a lot of additional attributes for nodes which are integral to the network. This ap-

    +

    proach is chosen because the networkx module does not allow updates to a dict-of-a-dict type +of attributes - i.e. if these attributes were to be combined in a 1st-level key ‘shortest-paths’, the entire content would have to be copied every time a new node-pair attribute is added, which would make the function extremely inefficient.

    +

    This is an important prerequisite function for the calculation of betweenness centrality.

    Parameters
    @@ -954,7 +974,9 @@

    vre/src/main/py considered. WARNING: this may be extremely ressource-intensive !

  • approximate (bool) – Flag indicating whether to consider all shortest paths in the network (False, default) or approximate the betweenness statistic using the -max_path_length argument.

    • +max_path_length argument. Note that if this is set to False, attributes of all +nodes will be written to file so as to avoid memory overflows. This requires a +preceding call to self.write_node_files().

  • max_path_length (int) – Maximum path length to consider for pairs of nodes when approximate == True. If set to None (default), all possible shortest paths will be considered.

    • @@ -964,7 +986,7 @@

    vre/src/main/py
    -write_node_files(node_attributes)
    +write_node_files(attribute_subset=None, export_path=None)

    Writes a JSON representation of all nodes in the network to file.

    For each node in the network, this function will write a JSON representation containing a dictionary of node_data_dict[keys], where keys are all entries in node_attributes if that key is actually found in the node @@ -977,8 +999,13 @@

    vre/src/main/py
    Parameters
    -

    node_attributes (list) – List of keys in a node’s attribute_dict to be included in the written JSON -representation of the node.

    +
      +

    • attribute_subset (list) – List of keys in a node’s attribute_dict to be included in the written JSON +representation of the node. If set to None (the default), all attributes will +be included.

      • +

    • export_path (str) – Path to which node files will be written. If set to None (the default), all +files will be written to self.data_dir.

      • +

    @@ -996,10 +1023,10 @@

    vre/src/main/py diff --git a/spitalhygiene/Docs/source/Unused.rst b/spitalhygiene/Docs/source/Unused.rst new file mode 100644 index 0000000..bf77846 --- /dev/null +++ b/spitalhygiene/Docs/source/Unused.rst @@ -0,0 +1,6 @@ +************************ +``Unused`` folder +************************ + +This folder contains archived files and scripts no longer used in the current version of the VRE model. + diff --git a/spitalhygiene/Docs/source/VRE_Model.rst b/spitalhygiene/Docs/source/VRE_Model.rst index 669f4e5..84f8a6b 100644 --- a/spitalhygiene/Docs/source/VRE_Model.rst +++ b/spitalhygiene/Docs/source/VRE_Model.rst @@ -5,7 +5,7 @@ VRE Model Overview Welcome to the VRE Model Documentation! The VRE model is documented with `Sphinx` on a `per-folder` basis. The root folder for this documentation is the -``spitalhygiene`` folder of the ``insel`` mono-repo on BitBucket. +``spitalhygiene`` folder of the ``vre-data-science`` repository on the Insel GitHub. diff --git a/spitalhygiene/Docs/source/conf.py b/spitalhygiene/Docs/source/conf.py index a9bf449..d0d1dbf 100644 --- a/spitalhygiene/Docs/source/conf.py +++ b/spitalhygiene/Docs/source/conf.py @@ -17,6 +17,7 @@ sys.path.insert(0, os.path.abspath('../../resources')) sys.path.insert(0, os.path.abspath('../../vre')) sys.path.insert(0, os.path.abspath('../../vre/src/main/python/vre')) +sys.path.insert(0, os.path.abspath('../../vre/src/main/python/vre/model')) # -- Project information ----------------------------------------------------- diff --git a/spitalhygiene/Docs/source/index.rst b/spitalhygiene/Docs/source/index.rst index f06dcca..fa32ed8 100644 --- a/spitalhygiene/Docs/source/index.rst +++ b/spitalhygiene/Docs/source/index.rst @@ -12,14 +12,10 @@ Welcome to the VRE Model documentation! VRE_Model.rst resources.rst sql.rst + Unused.rst vre.rst + model.rst Patient_Test_Data.rst -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/spitalhygiene/Docs/source/model.rst b/spitalhygiene/Docs/source/model.rst new file mode 100644 index 0000000..0d52e1f --- /dev/null +++ b/spitalhygiene/Docs/source/model.rst @@ -0,0 +1,65 @@ +***************************************** +``vre/src/main/python/vre/model`` folder +***************************************** + +This folder contains the definitions for all classes used in the VRE model. + + +----- + +File: Appointment.py +=============================== + +.. automodule:: Appointment + :members: + + +File: Bed.py +=============================== + +.. automodule:: Bed + :members: + + +File: Care.py +=============================== + +.. automodule:: Care + :members: + + +File: Chop.py +=============================== + +.. automodule:: Chop + :members: + + +File: Devices.py +=============================== + +.. automodule:: Device + :members: + + +File: Employee.py +=============================== + +.. automodule:: Employee + :members: + + +File: ICD.py +=============================== + +.. automodule:: ICD + :members: + + +File: Medication.py +=============================== + +.. automodule:: Medication + :members: + + diff --git a/spitalhygiene/Docs/source/resources.rst b/spitalhygiene/Docs/source/resources.rst index 86816d6..a0a0813 100644 --- a/spitalhygiene/Docs/source/resources.rst +++ b/spitalhygiene/Docs/source/resources.rst @@ -18,4 +18,11 @@ File: Query_Atelier_Data.py =============================== .. automodule:: Query_Atelier_Data - :members: \ No newline at end of file + :members: + + +File: preprocesor.py +=============================== + +.. automodule:: preprocessor + :members: diff --git a/spitalhygiene/resources/AM aus PDMS_VRE_2018-08-18_fg.xlsx b/spitalhygiene/Unused/AM aus PDMS_VRE_2018-08-18_fg.xlsx similarity index 100% rename from spitalhygiene/resources/AM aus PDMS_VRE_2018-08-18_fg.xlsx rename to spitalhygiene/Unused/AM aus PDMS_VRE_2018-08-18_fg.xlsx diff --git a/spitalhygiene/resources/BWTYP-BWART.csv b/spitalhygiene/Unused/BWTYP-BWART.csv similarity index 100% rename from spitalhygiene/resources/BWTYP-BWART.csv rename to spitalhygiene/Unused/BWTYP-BWART.csv diff --git a/spitalhygiene/resources/PDMS_ATC.csv b/spitalhygiene/Unused/PDMS_ATC.csv similarity index 100% rename from spitalhygiene/resources/PDMS_ATC.csv rename to spitalhygiene/Unused/PDMS_ATC.csv diff --git a/spitalhygiene/resources/query_device.sql b/spitalhygiene/Unused/query_device.sql similarity index 100% rename from spitalhygiene/resources/query_device.sql rename to spitalhygiene/Unused/query_device.sql diff --git a/spitalhygiene/resources/tree_all.html b/spitalhygiene/Unused/tree_all.html similarity index 100% rename from spitalhygiene/resources/tree_all.html rename to spitalhygiene/Unused/tree_all.html diff --git a/spitalhygiene/resources/tree_devices.html b/spitalhygiene/Unused/tree_devices.html similarity index 100% rename from spitalhygiene/resources/tree_devices.html rename to spitalhygiene/Unused/tree_devices.html diff --git a/spitalhygiene/resources/tree_employees.html b/spitalhygiene/Unused/tree_employees.html similarity index 100% rename from spitalhygiene/resources/tree_employees.html rename to spitalhygiene/Unused/tree_employees.html diff --git a/spitalhygiene/resources/tree_nomed.html b/spitalhygiene/Unused/tree_nomed.html similarity index 100% rename from spitalhygiene/resources/tree_nomed.html rename to spitalhygiene/Unused/tree_nomed.html diff --git a/spitalhygiene/resources/tree_rooms.html b/spitalhygiene/Unused/tree_rooms.html similarity index 100% rename from spitalhygiene/resources/tree_rooms.html rename to spitalhygiene/Unused/tree_rooms.html diff --git a/spitalhygiene/resources/umgebungsabklaerung.cypher b/spitalhygiene/Unused/umgebungsabklaerung.cypher similarity index 100% rename from spitalhygiene/resources/umgebungsabklaerung.cypher rename to spitalhygiene/Unused/umgebungsabklaerung.cypher diff --git a/spitalhygiene/sql/tests/V_VRE_SCREENING_DATA.sql b/spitalhygiene/sql/tests/V_VRE_SCREENING_DATA.sql new file mode 100644 index 0000000..00395f9 --- /dev/null +++ b/spitalhygiene/sql/tests/V_VRE_SCREENING_DATA.sql @@ -0,0 +1,17 @@ +SELECT [auftrag_nr] + ,[erfassung] + ,[entnahme] + ,[vorname] + ,[nachname] + ,[geburtsdatum] + ,[patient_nr] + ,[pruefziffer] + ,[patient_id] + ,[auftraggeber] + ,[kostenstelle] + ,[material_typ] + ,[transport] + ,[resultat] + ,[analyse_methode] + ,[screening_context] +FROM [Atelier_DataScience].[dbo].[V_VRE_SCREENING_DATA] \ No newline at end of file diff --git a/spitalhygiene/sql/vre/deleted_screenings.sql b/spitalhygiene/sql/vre/OBSOLETE_deleted_screenings.sql similarity index 100% rename from spitalhygiene/sql/vre/deleted_screenings.sql rename to spitalhygiene/sql/vre/OBSOLETE_deleted_screenings.sql diff --git a/spitalhygiene/sql/vre/V_VRE_SCREENING_DATA.sql b/spitalhygiene/sql/vre/V_VRE_SCREENING_DATA.sql new file mode 100644 index 0000000..00395f9 --- /dev/null +++ b/spitalhygiene/sql/vre/V_VRE_SCREENING_DATA.sql @@ -0,0 +1,17 @@ +SELECT [auftrag_nr] + ,[erfassung] + ,[entnahme] + ,[vorname] + ,[nachname] + ,[geburtsdatum] + ,[patient_nr] + ,[pruefziffer] + ,[patient_id] + ,[auftraggeber] + ,[kostenstelle] + ,[material_typ] + ,[transport] + ,[resultat] + ,[analyse_methode] + ,[screening_context] +FROM [Atelier_DataScience].[dbo].[V_VRE_SCREENING_DATA] \ No newline at end of file diff --git a/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py b/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py index 8e0bfaa..f28a9e2 100644 --- a/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py +++ b/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py @@ -69,7 +69,7 @@ def __init__(self, hdfs_pipe=True): self.surgery_path = os.path.join(self.base_path, "LA_ISH_NICP.csv") self.tacs_path = os.path.join(self.base_path, "TACS_DATEN.csv") self.icd_path = os.path.join(self.base_path, "LA_ISH_NDIA_NORM.csv") - self.VRE_screenings_path = os.path.join(self.base_path, "VRE_Screenings_Final.csv") + self.VRE_screenings_path = os.path.join(self.base_path, "V_VRE_SCREENING_DATA.csv") self.hdfs_pipe = hdfs_pipe # binary attribute specifying whether to read data Hadoop (True) or CSV (False) @@ -136,7 +136,8 @@ def patient_data(self, risk_only=False): "wards" :math:`\\longrightarrow` *Wards*, etc. } - Please refer to the ``vre/src/main/python/vre/model`` folder documentation for more details. + Please refer to the ``vre/src/main/python/vre/model`` folder documentation for more details on the + various objects. """ rooms = dict() # dictionary mapping room names to Room() objects --> {'BH N 123' : Room(), ... } wards = dict() diff --git a/spitalhygiene/vre/src/main/python/vre/data_compiler.py b/spitalhygiene/vre/src/main/python/vre/data_compiler.py index e09d14d..5a79035 100644 --- a/spitalhygiene/vre/src/main/python/vre/data_compiler.py +++ b/spitalhygiene/vre/src/main/python/vre/data_compiler.py @@ -19,7 +19,6 @@ from HDFS_data_loader import HDFS_data_loader from feature_extractor import feature_extractor from networkx_graph import surface_model, create_model_snapshots -import preprocessor import logging import os import datetime diff --git a/spitalhygiene/vre/src/main/python/vre/feature_extractor.py b/spitalhygiene/vre/src/main/python/vre/feature_extractor.py index 18eb315..47e6672 100644 --- a/spitalhygiene/vre/src/main/python/vre/feature_extractor.py +++ b/spitalhygiene/vre/src/main/python/vre/feature_extractor.py @@ -8,9 +8,9 @@ ----- """ -from sklearn.feature_extraction import DictVectorizer -import numpy as np -import pandas as pd +# from sklearn.feature_extraction import DictVectorizer +# import numpy as np +# import pandas as pd import os import datetime from dateutil import relativedelta diff --git a/spitalhygiene/vre/src/main/python/vre/model/Appointment.py b/spitalhygiene/vre/src/main/python/vre/model/Appointment.py index 27ebf17..7ddad92 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Appointment.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Appointment.py @@ -1,23 +1,18 @@ +# -*- coding: utf-8 -*- +"""This script contains the ``Appointment`` class used in the VRE model. + +----- +""" + from datetime import datetime import logging class Appointment: - """ - Models an appointment from RAP. - Schema: [TERMINID],[IS_DELETED],[TERMINBEZEICHNUNG],[TERMINART],[TERMINTYP],[TERMINDATUM],[DAUERINMIN] + """Models an appointment from RAP. """ - def __init__( - self, - termin_id, - is_deleted, - termin_bezeichnung, - termin_art, - termin_typ, - termin_datum, - dauer_in_min, - ): + def __init__(self, termin_id, is_deleted, termin_bezeichnung, termin_art, termin_typ, termin_datum, dauer_in_min): self.termin_id = termin_id self.is_deleted = is_deleted self.termin_bezeichnung = termin_bezeichnung @@ -34,24 +29,51 @@ def __init__( self.rooms = [] def add_device(self, device): + """Adds a device to the self.devices() list of this appointment. + + Args: + device (Device() Object): Device() object to append to this appointment. + """ self.devices.append(device) def add_room(self, room): + """Adds a room to the self.rooms() list of this appointment. + + Args: + room (Room() Object): Room() object to append to this appointment. + """ self.rooms.append(room) def add_employee(self, employee): + """Adds an employee to the self.employees() list of this appointment. + + Args: + employee (Employee() Object): Employee() object to append to this appointment. + """ self.employees.append(employee) + @staticmethod def create_termin_map(lines): - """ - Loads the appointments from csv reader. - This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: - >> TABLE NAME: V_DH_DIM_TERMIN_CUR - ["TERMINID", "IS_DELETED", "TERMINBEZEICHNUNG", "TERMINART", "TERMINTYP", "TERMINDATUM", "DAUERINMIN"] - ["957219", "0", "K90 HINF", "K90 HINF", "Patiententermin", "2005-02-04 00:00:00.0000", "90.00000"] - ["957224", "0", "Konsultation 15'", "Konsultation 15'", "Patiententermin", "2005-02-03 00:00:00.0000", "15.00000"] - - Returns: A dictionary mapping appointment ids to Appointment() objects --> { '36830543' : Appointment(), ... } + """Loads the appointments from a csv reader instance. + + This function will be called by the ``HDFS_data_loader.patient_data()`` function (lines is an iterator object). + The underlying table in the Atelier_DataScience is named ``V_DH_DIM_TERMIN_CUR`` and structured as follows: + + ======== ========== ================= ================ =============== ======================== ========== + TERMINID IS_DELETED TERMINBEZEICHNUNG TERMINART TERMINTYP TERMINDATUM DAUERINMIN + ======== ========== ================= ================ =============== ======================== ========== + 957219 0 K90 HINF K90 HINF Patiententermin 2005-02-04 00:00:00.0000 90.00000 + 957224 0 Konsultation 15' Konsultation 15' Patiententermin 2005-02-03 00:00:00.0000 15.00000 + ======== ========== ================= ================ =============== ======================== ========== + + Args: + lines (iterator() object): csv iterator from which data will be read + + Returns: + dict: + Dictionary mapping appointment ids to Appointment() objects + + --> ``{ '36830543' : Appointment(), ... }`` """ logging.debug("create_termin_map") nr_malformed = 0 @@ -68,17 +90,29 @@ def create_termin_map(lines): logging.info(f"{nr_ok} ok, {nr_malformed} appointments malformed") return appointments + @staticmethod def add_appointment_to_case(lines, cases, appointments): - """ - Adds Appointment() objects to the SAP cases based on lines read from a csv file. - This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: - >> TABLE NAME: V_DH_FACT_TERMINPATIENT - ["TERMINID", "PATIENTID", "FALLID"] - ["35672314", "00008210020", "0005660334"] - ["17255155", "00002042800", "0004017880"] - - :param cases: Dictionary mapping case ids to Case() objects --> {"0003536421" : Case(), "0003473241" : Case(), ...} - :param appointments: Dictionary mapping appointment ids to Appointment() objects --> { '36830543' : Appointment(), ... } + """Adds Appointment() objects to the SAP cases based on lines read from a csv file. + + This function will be called by the ``HDFS_data_loader.patient_data()`` function (lines is an iterator object). + The underlying table in the Atelier_DataScience is called ``V_DH_FACT_TERMINPATIENT`` and structured as follows: + + ======== =========== ========== + TERMINID PATIENTID FALLID + ======== =========== ========== + 35672314 00008210020 0005660334 + 17255155 00002042800 0004017880 + ======== =========== ========== + + Args: + lines (iterator() object): csv iterator from which data will be read + cases (dict): Dictionary mapping case ids to Case() objects + + --> ``{ "0003536421" : Case(), "0003473241" : Case(), ...}`` + + appointments (dict): Dictionary mapping appointment ids to Appointment() objects + + --> ``{ '36830543' : Appointment(), ... }`` """ logging.debug("add_appointment_to_case") nr_appointment_not_found = 0 @@ -95,6 +129,5 @@ def add_appointment_to_case(lines, cases, appointments): continue cases[case_id].add_appointment(appointments[appointment_id]) nr_ok += 1 - logging.info( - f"{nr_ok} ok, {nr_case_not_found} cases not found, {nr_appointment_not_found} appointments not found" - ) + logging.info(f"{nr_ok} ok, {nr_case_not_found} cases not found, " + f"{nr_appointment_not_found} appointments not found") diff --git a/spitalhygiene/vre/src/main/python/vre/model/Bed.py b/spitalhygiene/vre/src/main/python/vre/model/Bed.py index d9f2702..bfd36fa 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Bed.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Bed.py @@ -1,8 +1,22 @@ +# -*- coding: utf-8 -*- +"""This script contains the ``Bed`` class used in the VRE model. + +----- +""" + from datetime import datetime class Bed: + """Models a Bed. + """ def __init__(self, name): self.name = name self.moves = [] + def add_move(self, move): - self.moves.append(move) \ No newline at end of file + """Adds a move to the self.moves() list of this bed. + + Args: + move (Move() Object): Move() object to append. + """ + self.moves.append(move) diff --git a/spitalhygiene/vre/src/main/python/vre/model/Care.py b/spitalhygiene/vre/src/main/python/vre/model/Care.py index 930bf59..4d62a71 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Care.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Care.py @@ -1,12 +1,15 @@ +# -*- coding: utf-8 -*- +"""This script contains the ``Care`` class used in the VRE model. + +----- +""" + import logging from datetime import datetime from Employee import Employee - class Care: - """ - This represents an entry in TACS. - Schema: patient_patientid],[patient_typ],[patient_status],[fall_nummer],[fall_typ],[fall_status],[datum_betreuung],[dauer_betreuung_in_min],[mitarbeiter_personalnummer],[mitarbeiter_anstellungsnummer],[mitarbeiter_login],[BATCH_RUN_ID] + """Models an entry in TACS. """ def __init__( @@ -36,23 +39,38 @@ def __init__( self.employee = None def add_employee(self, employee): + """Assigns an employee to the ``self.employee`` attribute. + + Note: + Only one employee can be assigned to Care() objects! + + Args: + employee (Employee() Object): Employee() object to assign. + """ self.employee = employee + @staticmethod def add_care_to_case(lines, cases, employees): - """ - Adds the entries from TACS as instances of Care() to the respective Case(). - This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: - >> TABLE NAME: TACS_DATEN - ["patient_patientid", "patient_typ", "patient_status", "fall_nummer", "fall_typ", "fall_status", "datum_betreuung", "dauer_betreuung_in_min", ... - ["00013768220", "Standard Patient", "aktiv", "0006422111", "Standard Fall", "aktiv", "2018-03-11 00:00:00.0", "3", ... - ["00000552828", "Standard Patient", "aktiv", "0006454306", "Standard Fall", "aktiv", "2018-04-10 00:00:00.0", "20", ... - <-- continued --> - "mitarbeiter_personalnummer", "mitarbeiter_anstellungsnummer", "mitarbeiter_login", "BATCH_RUN_ID"] - "0301119", "00026556", "I0301119", "870"] - "0025908", "00014648", "I0025908", "870"] - - :param cases: Dictionary mapping case ids to Case() objects --> {"0003536421" : Case(), "0003473241" : Case(), ...} - :param employees: Dictionary mapping employee_ids to Employee() objects --> {'0032719' : Employee(), ... } + """Adds the entries from TACS as instances of Care() objects to the respective Case(). + + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). + The underlying table in the Atelier_DataScience is called ``TACS_DATEN`` and structured as follows: + + ================= ================ ============== =========== ============= =========== ===================== ====================== ========================== ============================= ================= ============ + patient_patientid patient_typ patient_status fall_nummer fall_typ fall_status datum_betreuung dauer_betreuung_in_min mitarbeiter_personalnummer mitarbeiter_anstellungsnummer mitarbeiter_login BATCH_RUN_ID + ================= ================ ============== =========== ============= =========== ===================== ====================== ========================== ============================= ================= ============ + 00013768220 Standard Patient aktiv 0006422111 Standard Fall aktiv 2018-03-11 00:00:00.0 3 0301119 00026556 I0301119 870 + 00000552828 Standard Patient aktiv 0006454306 Standard Fall aktiv 2018-04-10 00:00:00.0 20 0025908 00014648 I0025908 870 + ================= ================ ============== =========== ============= =========== ===================== ====================== ========================== ============================= ================= ============ + + Args: + lines (iterator() object): csv iterator from which data will be read + cases (dict): Dictionary mapping case ids to Case() objects + + --> ``{"0003536421" : Case(), "0003473241" : Case(), ...}`` + employees (dict): Dictionary mapping employee_ids to Employee() objects + + --> ``{'0032719' : Employee(), ... }`` """ logging.debug("add_care_to_case") nr_case_not_found = 0 @@ -78,6 +96,5 @@ def add_care_to_case(lines, cases, employees): employees[employee.mitarbeiter_id] = employee care.add_employee(employee) - logging.info( - f"{nr_case_not_found} cases not found, {nr_employee_created} employees created, {nr_employee_found} employees found" - ) + logging.info(f"{nr_case_not_found} cases not found, " + f"{nr_employee_created} employees created, {nr_employee_found} employees found") diff --git a/spitalhygiene/vre/src/main/python/vre/model/Chop.py b/spitalhygiene/vre/src/main/python/vre/model/Chop.py index 1a1d44a..1ab4551 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Chop.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Chop.py @@ -1,6 +1,14 @@ +# -*- coding: utf-8 -*- +"""This script contains the ``CHOP`` class used in the VRE model. + +----- +""" + import logging class Chop: + """Models a ``CHOP`` code. + """ def __init__( self, chop_code, @@ -42,16 +50,19 @@ def __init__( self.cases = [] # Keep track of all cases that have this chop code def add_case(self, case): - """ - Adds a new case to this chop code. + """Adds a case to the self.cases list. + + Args: + case (Case() Object): Case() object to append. """ self.cases.append(case) def get_detailed_chop(self): - ''' - Get the description text from the highest available level for this code. - :return: - ''' + """Returns description text from the highest available level for this CHOP. + + Returns: + str: Highest available level for this CHOP code. + """ for field in [ self.chop_level6, self.chop_level5, @@ -64,43 +75,57 @@ def get_detailed_chop(self): return field def get_lowest_level_code(self): - ''' - The level 2 code represents the first number of the code. - E.g.: Z89.07.24 -> Z89 - :return: - ''' + """Returns the lowest level of the CHOP code. + + The lowest level is is level 2 and represented as the *first number* of the Code: + + ``Z89.07.24`` :math:`\\longrightarrow` ``Z89`` + + Returns: + str: Lowest available level for this CHOP code. + """ return self.chop_code_level2 + @staticmethod def create_chop_dict(lines): - """ - Creates and returns a dict of all chop codes. The key of a chop code is _ (different catalogs exist for different years). - This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: - >> TABLE NAME: V_DH_REF_CHOP - ["CHOPCODE", "CHOPVERWENDUNGSJAHR", "CHOP", "CHOPCODELEVEL1", "CHOPLEVEL1", "CHOPCODELEVEL2", ... - ["Z62.99.30", "2016", "Entnahme von Hoden- oder Neben...", "C11", "Operationen an den männlichen Geschlechtsorganen (60–64)", "Z62", ... - ["Z62.99.99", "2011", "Sonst. Operationen an den Ho", "C9", "Operationen am Verdauungstrakt (42–54)", "Z62", ... - <-- continued --> - "CHOPLEVEL2", "CHOPCODELEVEL3", "CHOPLEVEL3", "CHOPCODELEVEL4", "CHOPLEVEL4", "CHOPCODELEVEL5", "CHOPLEVEL5", ... - "Operationen an den Hoden", "Z62.9", "Sonstige Operationen an den Hoden", "Z62.99", "Sonstige Operationen an den Hoden", "Z62.99.0", "Detail der Subkategorie 62.99", ... - "Operationen an den Hoden", "Z62.9", "Sonstige Operationen an den Hoden", "Z62.99", "Sonstige Operationen an den Hoden", "Z62.99.99", "Sonstige Operationen an den Hoden, sonstige", ... - <-- continued --> - "CHOPCODELEVEL6", "CHOPLEVEL6", "CHOPSTATUS", "CHOPSAPKATALOGID"] - "Z62.99.30", "Entnahme von Hoden- oder Nebenhodengewebe zur Aufbereitung für die künstliche Insemination", "0", "16"] - "", "", "1", "10"] - - :return Dictionary mapping the chopcode_katalogid entries to Chop() obects --> { 'Z39.61.10_11': Chop(), ... } + """Creates and returns a dict of all chop codes. + + The key of a chop code is ``_`` - different catalogs exist for different years. This function + will be called by the ``HDFS_data_loader.patient_data()`` function (lines is an iterator object). The underlying + table in Atelier_DataScience is called ``V_DH_REF_CHOP`` and structured as follows: + + ========= =================== ================================= ============== =========================================================== ============== ======================== ============== ================================= ============== ================================= ============== ==================================== ============== ================================== ========== ================ + CHOPCODE CHOPVERWENDUNGSJAHR CHOP CHOPCODELEVEL1 CHOPLEVEL1 CHOPCODELEVEL2 CHOPLEVEL2 CHOPCODELEVEL3 CHOPLEVEL3 CHOPCODELEVEL4 CHOPLEVEL4 CHOPCODELEVEL5 CHOPLEVEL5 CHOPCODELEVEL6 CHOPLEVEL6 CHOPSTATUS CHOPSAPKATALOGID + ========= =================== ================================= ============== =========================================================== ============== ======================== ============== ================================= ============== ================================= ============== ==================================== ============== ================================== ========== ================ + Z62.99.30 2016 Entnahme von Hoden- oder Neben... C11 Operationen an den männlichen Geschlechtsorganen (60–64) Z62 Operationen an den Hoden Z62.9 Sonstige Operationen an den Hoden Z62.99 Sonstige Operationen an den Hoden Z62.99.0 Detail der Subkategorie 62.99 Z62.99.30 Entnahme von Hoden- oder Nebenh... 0 16 + Z62.99.99 2011 Sonst. Operationen an den Ho... C9 Operationen am Verdauungstrakt (42–54) Z62 Operationen an den Hoden Z62.9 Sonstige Operationen an den Hoden Z62.99 Sonstige Operationen an den Hoden Z62.99.99 Sonstige Operationen an den Hoden... 1 10 + ========= =================== ================================= ============== =========================================================== ============== ======================== ============== ================================= ============== ================================= ============== ==================================== ============== ================================== ========== ================ + + Args: + lines (iterator() object): csv iterator from which data will be read + + Returns: + dict: + Dictionary mapping the chopcode_katalogid entries to Chop() objects + + :math:`\\longrightarrow` ``{ 'Z39.61.10_11': Chop(), ... }`` """ logging.debug("create_chop_dict") chops = dict() for line in lines: chop = Chop(*line) - chops[chop.chop_code + "_" + chop.chop_sap_katalog_id] = chop # based on the schema in the docstring, this would yield "Z62.99.30_16" or "Z62.99.99_10" + chops[chop.chop_code + "_" + chop.chop_sap_katalog_id] = chop + # based on the schema in the docstring, this would yield "Z62.99.30_16" or "Z62.99.99_10" logging.info(f"{len(chops)} chops created") return chops def chop_code_stats(self, chops): - """ - Print frequency of different chop codes. + """Print frequency of different chop codes to console. + + Args: + chops (dict): Dictionary mapping the chopcode_katalogid entries to Chop() objects + + :math:`\\longrightarrow` ``{ 'Z39.61.10_11': Chop(), ... }`` """ for chop in chops.values(): print( diff --git a/spitalhygiene/vre/src/main/python/vre/model/Device.py b/spitalhygiene/vre/src/main/python/vre/model/Device.py index 0f0d714..6b42c23 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Device.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Device.py @@ -1,25 +1,42 @@ +# -*- coding: utf-8 -*- +"""This script contains the ``Device`` class used in the VRE model. + +----- +""" + import logging class Device: - ''' + """ Models a device from RAP. - Schema: GERAETID;GERAETNAME - ''' + """ def __init__(self, geraet_id, geraet_name): self.geraet_id = geraet_id self.geraet_name = geraet_name + @staticmethod def create_device_map(lines): - ''' - Loads all devices into a dictionary based on lines in the csv file. - This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: - >> TABLE NAME: V_DH_DIM_GERAET_CUR - ["GERAETID", "GERAETNAME"] - ["82250", "ANS-Fix"] - ["162101", "Waage"] - - Returns: A dictionary mapping device_ids to Device() object --> {'64174' : Device(), ... } - ''' + """Loads all devices into a dictionary based on lines in the csv file. + + This function will be called by the ``HDFS_data_loader.patient_data()`` function (lines is an iterator object). + The underlying table in the Atelier_DataScience is called ``V_DH_DIM_GERAET_CUR`` and structured as follows: + + ======== ========== + GERAETID GERAETNAME + ======== ========== + 82250 ANS-Fix + 162101 Waage + ======== ========== + + Args: + lines (iterator() object): csv iterator from which data will be read + + Returns: + dict: + Dictionary mapping device ids to Device() objects + + :math:`\\longrightarrow` ``{'64174' : Device(), ... }`` + """ logging.debug("create_device_map") devices = dict() for line in lines: @@ -28,18 +45,29 @@ def create_device_map(lines): logging.info(f"{len(devices)} devices created") return devices + @staticmethod def add_device_to_appointment(lines, appointments, devices): - ''' - Adds the device in devices to the respective appointment in appointments based on lines in the csv file. - This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: - >> TABLE NAME: V_DH_FACT_TERMINGERAET - ["TERMINID", "GERAETID", "TERMINSTART_TS", "TERMINENDE_TS", "DAUERINMIN"] - ["26266554", "123223", "2015-04-03 13:45:00.0000", "2015-04-03 15:45:00.0000", "120.000000"] - ["23678836", "38006", "2014-07-31 10:00:00.0000", "2014-07-31 10:30:00.0000", "30.000000"] - - :param appointments: Dictionary mapping appointment ids to Appointment() objects --> { '36830543' : Appointment(), ... } - :param devices: Dictionary mapping device_ids to Device() object --> {'64174' : Device(), ... } - ''' + """Adds the device in ``devices`` to the respective appointment in ``appointments``. + + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). + The underlying table in the Atelier_DataScience is called V_DH_FACT_TERMINGERAET and structured as follows: + + ======== ======== ======================== ======================== ========== + TERMINID GERAETID TERMINSTART_TS TERMINENDE_TS DAUERINMIN + ======== ======== ======================== ======================== ========== + 26266554 123223 2015-04-03 13:45:00.0000 2015-04-03 15:45:00.0000 120.000000 + 23678836 38006 2014-07-31 10:00:00.0000 2014-07-31 10:30:00.0000 30.000000 + ======== ======== ======================== ======================== ========== + + Args: + lines (iterator() object): csv iterator from which data will be read + appointments (dict): Dictionary mapping appointment ids to Appointment() objects + + :math:`\\longrightarrow` ``{ '36830543' : Appointment(), ... }`` + devices (dict): Dictionary mapping device_ids to Device() objects + + :math:`\\longrightarrow` ``{'64174' : Device(), ... }`` + """ logging.debug("add_device_to_appointment") nr_appointment_not_found = 0 nr_device_not_found = 0 @@ -55,6 +83,5 @@ def add_device_to_appointment(lines, appointments, devices): continue appointments[appointment_id].add_device(devices[device_id]) nr_ok += 1 - logging.info( - f"{nr_ok} ok, {nr_appointment_not_found} appointments not found, {nr_device_not_found} devices not found" - ) + logging.info(f"{nr_ok} ok, {nr_appointment_not_found} appointments not found, " + f"{nr_device_not_found} devices not found") diff --git a/spitalhygiene/vre/src/main/python/vre/model/Employee.py b/spitalhygiene/vre/src/main/python/vre/model/Employee.py index 18d5a10..9594d55 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Employee.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Employee.py @@ -1,24 +1,42 @@ +# -*- coding: utf-8 -*- +"""This script contains the ``Employee`` class used in the VRE model. + +----- +""" + import logging class Employee: - """ - Models an employee (doctor, nurse, etc) from RAP. + """Models an employee (doctor, nurse, etc) from RAP. """ def __init__(self, mitarbeiter_id): self.mitarbeiter_id = mitarbeiter_id + @staticmethod def create_employee_map(lines): - """ - Read the appointment to employee file and create Employee() objects. - This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: - >> TABLE NAME: V_DH_FACT_TERMINMITARBEITER - ["TERMINID", "MITARBEITERID", "TERMINSTART_TS", "TERMINENDE_TS", "DAUERINMIN"] - ["521664", "0063239", "2003-11-11 07:30:00.000", "2003-11-11 08:00:00.0000", "30.000000"] - ["521754", "X33671", "2003-11-10 09:15:00.000", "2003-11-10 09:45:00.0000", "30.000000"] - - Returns: Dictionary mapping employee_ids to Employee() objects --> {'0032719' : Employee(), ... } + """Reads the appointment to employee file and creates an Employee(). + + + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). + The underlying table in the Atelier_DataScience is called V_DH_FACT_TERMINMITARBEITER and structured as follows: + + ======== ============= ======================= ======================== ========== + TERMINID MITARBEITERID TERMINSTART_TS TERMINENDE_TS DAUERINMIN + ======== ============= ======================= ======================== ========== + 521664 0063239 2003-11-11 07:30:00.000 2003-11-11 08:00:00.0000 30.000000 + 521754 X33671 2003-11-10 09:15:00.000 2003-11-10 09:45:00.0000 30.000000 + ======== ============= ======================= ======================== ========== + + Args: + lines (iterator() object): csv iterator from which data will be read + + Returns: + dict: + Dictionary mapping employee_ids to Employee() objects + + :math:`\\longrightarrow` ``{'0032719' : Employee(), ... }`` """ logging.debug("create_employee_map") employee_dict = dict() @@ -28,14 +46,22 @@ def create_employee_map(lines): logging.info(f"{len(employee_dict)} employees created") return employee_dict + @staticmethod def add_employee_to_appointment(lines, appointments, employees): - """ - Adds Employee() in employees to Appointment() in appointments based on the lines read from the csv file. + """Adds Employee() in employees to an Appointment(). + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). - The underlying table is V_DH_FACT_TERMINMITARBEITER, and is identical to the one defined in the create_employee_map() method above. + The underlying table is V_DH_FACT_TERMINMITARBEITER, and is identical to the one defined in the + create_employee_map() method above. + + Args: + lines (iterator() object): csv iterator from which data will be read + appointments (dict): Dictionary mapping appointment ids to Appointment() objects + + :math:`\\longrightarrow` ``{ '36830543' : Appointment(), ... }`` + employees (dict): Dictionary mapping employee_ids to Employee() objects - :param appointments: Dictionary mapping appointment ids to Appointment() objects --> { '36830543' : Appointment(), ... } - :param employees: Dictionary mapping employee_ids to Employee() objects --> {'0032719' : Employee(), ... } + :math:`\\longrightarrow` ``{'0032719' : Employee(), ... }`` """ logging.debug("add_employee_to_appointment") nr_employee_not_found = 0 @@ -52,6 +78,5 @@ def add_employee_to_appointment(lines, appointments, employees): continue appointments[appointment_id].add_employee(employees[employee_id]) nr_ok += 1 - logging.info( - f"{nr_ok} ok, {nr_appointment_not_found} appointments not found, {nr_employee_not_found} employees not found" - ) + logging.info(f"{nr_ok} ok, {nr_appointment_not_found} appointments not found, " + f"{nr_employee_not_found} employees not found") diff --git a/spitalhygiene/vre/src/main/python/vre/model/ICD.py b/spitalhygiene/vre/src/main/python/vre/model/ICD.py index a908954..24f3718 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/ICD.py +++ b/spitalhygiene/vre/src/main/python/vre/model/ICD.py @@ -1,32 +1,54 @@ +# -*- coding: utf-8 -*- +"""This script contains the ``ICD`` class used in the VRE model. + +----- +""" + import datetime import logging class ICD: + """Models an ``ICD`` object. + """ def __init__(self, falnr, dkey1, dkat, diadt, drg_cat): self.fall_nummer = falnr self.icd_code = dkey1 - self.catalogue_year = int(dkat) # catalogue year of the ICD code (2-digit integer) - may or may not be relevant - self.diagnosis_dt = datetime.datetime.strptime(diadt, '%Y-%m-%d') # date when ICD code was set - self.drg_category = drg_cat # single character specifying the DRG category + self.catalogue_year = int(dkat) # catalogue year of the ICD code (2-digit integer) - may or may not be relevant + self.diagnosis_dt = datetime.datetime.strptime(diadt, '%Y-%m-%d') # date when ICD code was set + self.drg_category = drg_cat # single character specifying the DRG category - self.cases = [] # List containing all cases with this particular ICD code (currently not used) + self.cases = [] # List containing all cases with this particular ICD code (currently not used) def add_case(self, case): - """ - Adds a case to this particular ICD code (currently not used). + """Adds a case to this ICD's ``self.cases`` attribute. + + Args: + case (Case() Object): Case() object to add. """ self.cases.append(case) + @staticmethod def create_icd_dict(lines): - """ - Creates and returns a dictionary of all icd codes. - This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: - >> TABLE NAME: LA_ISH_NDIA - ['FALNR', 'DKEY1', 'DKAT1', 'DIADT', 'DRG_CATEGORY'] - ['0001832682', 'A41.9', '17', '2018-02-27', 'P'] - ['0001832682', 'R65.1', '17', '2018-02-27', 'S'] - - :return: Dictionary mapping the icd_code entries to ICD() objects --> { 'Z12.8': ICD(), ... } + """Creates and returns a dictionary of all icd codes. + + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). + The underlying table in the Atelier_DataScience is LA_ISH_NDIA and structured as follows: + + ============ ======= ======= ============ ============== + 'FALNR' 'DKEY1' 'DKAT1' 'DIADT' 'DRG_CATEGORY' + ============ ======= ======= ============ ============== + '0001832682' 'A41.9' '17' '2018-02-27' 'P' + '0001832682' 'R65.1' '17' '2018-02-27' 'S' + ============ ======= ======= ============ ============== + + Args: + lines (iterator() object): csv iterator from which data will be read + + Returns: + dict: + Dictionary mapping the icd_code entries to ICD() objects + + :math:`\\longrightarrow` ``{ 'Z12.8': ICD(), ... }`` """ logging.debug("Creating ICD dictionary") icd_dict = {} @@ -38,12 +60,17 @@ def create_icd_dict(lines): logging.info(f'Successfully created {len(icd_dict.values())} ICD entries') return icd_dict + @staticmethod def add_icd_to_case(lines, cases): - """ - Adds ICD codes to cases based on the ICD.fall_nummer attribute. For details on how each line in the lines iterator object is formatted, see the function create_icd_dict() above. + """Adds ICD codes to cases based on the ICD.fall_nummer attribute. + + For details on how each line in the lines iterator object is formatted, please refer to the function + create_icd_dict() above. - :param lines: Iterator object returning data directly from csv - :param cases: Dictionary mapping case ids to Case() objects --> {"0003536421" : Case(), "0003473241" : Case(), ...} + Args: + lines (iterator() object): csv iterator from which data will be read + cases (dict): Dictionary mapping case ids to Case() objects + :math:`\\longrightarrow` ``{"0003536421" : Case(), "0003473241" : Case(), ...}`` """ logging.debug("Adding ICD codes to cases") cases_not_found = 0 @@ -58,7 +85,8 @@ def add_icd_to_case(lines, cases): unique_case_ids.append(cases.get(this_icd.fall_nummer)) else: cases_not_found += 1 - logging.info(f'Added {cases_found} ICD codes to {len(set(unique_case_ids))} relevant cases, {cases_not_found} cases not found') + logging.info(f'Added {cases_found} ICD codes to {len(set(unique_case_ids))} relevant cases,' + f'{cases_not_found} cases not found') diff --git a/spitalhygiene/vre/src/main/python/vre/model/Medication.py b/spitalhygiene/vre/src/main/python/vre/model/Medication.py index 7cdc298..941881f 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Medication.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Medication.py @@ -1,8 +1,16 @@ +# -*- coding: utf-8 -*- +"""This script contains the ``Medication`` class used in the VRE model. + +----- +""" + from datetime import datetime import logging class Medication: + """Models a ``Medication`` object. + """ def __init__( self, patient_id, @@ -21,27 +29,40 @@ def __init__( self.drug_quantity = drug_quantity self.drug_unit = drug_unit self.drug_dispform = drug_dispform - self.drug_submission = datetime.strptime( - drug_submission, "%Y-%m-%d %H:%M:%S" - ) + self.drug_submission = datetime.strptime(drug_submission, "%Y-%m-%d %H:%M:%S") def is_antibiotic(self): - """ - Does the ATC code start with J01? - :return: boolean + """Returns the antibiotic status of a Medication. + + Antibiotics are identified via the prefix ``J01``. + + Returns: + bool: Whether or not the medication ``self.drug_atc`` attribute starts with ``J01``. """ return self.drug_atc.startswith("J01") + @staticmethod def create_drug_map(lines): - """ - Creates a dictionary of ATC codes to human readable drug names based on the entries in the CSV file. - This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). The underlying table is structured as follows: - >> TABLE NAME: V_LA_IPD_DRUG_NORM - ["PATIENTID", "CASEID", "DRUG_TEXT", "DRUG_ATC", "DRUG_QUANTITY", "DRUG_UNIT", "DRUG_DISPFORM", "DRUG_SUBMISSION"] - ["00001711342", "0006437617", "Torem Tabl 10 mg (Torasemid)", "C03CA04", "2.0000000000000", "Stk", "p.o.", "2018-03-24 09:52:28.0000000"] - ["00001711342", "0006437617", "Ecofenac Sandoz Lipogel 1 % (Diclofenac)", "M02AA15", "1.0000000000000", "Dos", "lokal / topisch", "2018-03-24 09:52:28.0000000"] - - Returns: A dictionary mapping drug codes to their respective text description --> {'B02BA01' : 'NaCl Braun Inf Lös 0.9 % 500 ml (Natriumchlorid)', ... } + """Creates a dictionary of ATC codes to human readable drug names. + + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). + The underlying table in the Atelier_DataScience is called V_LA_IPD_DRUG_NORM and structured as follows: + + =========== ========== ======================================== ======== =============== ========= =============== =========================== + PATIENTID CASEID DRUG_TEXT DRUG_ATC DRUG_QUANTITY DRUG_UNIT DRUG_DISPFORM DRUG_SUBMISSION + =========== ========== ======================================== ======== =============== ========= =============== =========================== + 00001711342 0006437617 Torem Tabl 10 mg (Torasemid) C03CA04 2.0000000000000 Stk p.o. 2018-03-24 09:52:28.0000000 + 00001711342 0006437617 Ecofenac Sandoz Lipogel 1 % (Diclofenac) M02AA15 1.0000000000000 Dos lokal / topisch 2018-03-24 09:52:28.0000000 + =========== ========== ======================================== ======== =============== ========= =============== =========================== + + Args: + lines (iterator() object): csv iterator from which data will be read + + Returns: + dict: + Dictionary mapping drug codes to their respective text description + + :math:`\\longrightarrow` ``{'B02BA01' : 'NaCl Braun Inf Lös 0.9 % 500 ml (Natriumchlorid)', ... }`` """ logging.debug("create_drug_map") drugs = dict() @@ -51,13 +72,19 @@ def create_drug_map(lines): logging.info(f"{len(drugs)} drugs created") return drugs + @staticmethod def add_medication_to_case(lines, cases): - """ - Read all the medication entries from the V_LA_IPD_DRUG_NORM table, and adds the drug (Medication() object) to the corresponding Case() (in cases) via Case.add_medication(). - This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object). - For a schema of the V_LA_IPD_DRUG_NORM table, see the function create_drug_map() above. + """Adds Medication() objects to Case() objects. + + This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator object), and + will add all Medication() objects to their corresponding Case() object in cases. The underlying table is + identical to the one used in the ``create_drug_map`` function. + + Args: + lines (iterator() object): csv iterator from which data will be read + cases (dict): Dictionary mapping case ids to Case() - :param cases: Dictionary mapping case ids to Case() --> {'0005976205' : Case(), ... } + :math:`\\longrightarrow` ``{'0005976205' : Case(), ... }`` """ logging.debug("add_medication_to_case") nr_cases_not_found = 0 diff --git a/spitalhygiene/vre/src/main/python/vre/networkx_graph.py b/spitalhygiene/vre/src/main/python/vre/networkx_graph.py index 417d0c0..687bdfd 100644 --- a/spitalhygiene/vre/src/main/python/vre/networkx_graph.py +++ b/spitalhygiene/vre/src/main/python/vre/networkx_graph.py @@ -991,7 +991,7 @@ def export_shortest_path_length_overview(self, focus_nodes=None, csv_sep=';', ex Args: focus_nodes (list or None): List of nodes considered for distribution. csv_sep (str): Separator used in export file. - export_path (str): Path to which node files will be written. If set to `None` (the default), the + export_path (str): Path to which node files will be written. If set to `None` (the default), the exported file will be written to `self.data_dir`. """ From 560a5910cc507ddecb7cc4a55fdd40a7ced52af3 Mon Sep 17 00:00:00 2001 From: Stefan Zahnd Date: Fri, 2 Aug 2019 13:29:25 +0200 Subject: [PATCH 8/9] Updated SQL queries for data extraction --- spitalhygiene/sql/vre/WARD_SCREENINGS.sql | 4 ++++ spitalhygiene/vre/src/main/python/vre/BasicConfig.ini | 4 +++- spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py | 7 +++++-- spitalhygiene/vre/src/main/python/vre/model/Case.py | 6 +++++- spitalhygiene/vre/src/main/python/vre/model/Medication.py | 2 +- spitalhygiene/vre/src/main/python/vre/model/Patient.py | 6 +++++- 6 files changed, 23 insertions(+), 6 deletions(-) create mode 100644 spitalhygiene/sql/vre/WARD_SCREENINGS.sql diff --git a/spitalhygiene/sql/vre/WARD_SCREENINGS.sql b/spitalhygiene/sql/vre/WARD_SCREENINGS.sql new file mode 100644 index 0000000..e1ecd3f --- /dev/null +++ b/spitalhygiene/sql/vre/WARD_SCREENINGS.sql @@ -0,0 +1,4 @@ +SELECT [screening_day] + ,[org_pf] + ,[screening_type] +FROM [Atelier_DataScience].[dbo].[WARD_SCREENINGS] diff --git a/spitalhygiene/vre/src/main/python/vre/BasicConfig.ini b/spitalhygiene/vre/src/main/python/vre/BasicConfig.ini index 5ac0aa4..95a7382 100644 --- a/spitalhygiene/vre/src/main/python/vre/BasicConfig.ini +++ b/spitalhygiene/vre/src/main/python/vre/BasicConfig.ini @@ -35,6 +35,8 @@ csv_sep = , [PARAMETERS] -# Indicator for data to use, one of 'test' (will load test patient only) or 'all' (data for all patients) +# Indicator for data to use, one of 'test' (will load test patient data only) or 'all' (data for all patients) data_basis = all +# Number of patients and cases to be loaded, either None (load all data) or any positive integer +load_limit = None diff --git a/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py b/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py index f28a9e2..65b1d45 100644 --- a/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py +++ b/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py @@ -70,6 +70,7 @@ def __init__(self, hdfs_pipe=True): self.tacs_path = os.path.join(self.base_path, "TACS_DATEN.csv") self.icd_path = os.path.join(self.base_path, "LA_ISH_NDIA_NORM.csv") self.VRE_screenings_path = os.path.join(self.base_path, "V_VRE_SCREENING_DATA.csv") + self.VRE_ward_screenings_path = os.path.join(self.base_path, "WARD_SCREENINGS.csv") self.hdfs_pipe = hdfs_pipe # binary attribute specifying whether to read data Hadoop (True) or CSV (False) @@ -148,12 +149,14 @@ def patient_data(self, risk_only=False): # Load Patient data from table: V_DH_DIM_PATIENT_CUR logging.info("loading patient data") patients = Patient.create_patient_dict(self.get_hdfs_pipe(self.patients_path) if self.hdfs_pipe is True - else self.get_csv_file(self.patients_path)) + else self.get_csv_file(self.patients_path), + load_limit=1000) # Load Case data from table: V_LA_ISH_NFAL_NORM logging.info("loading case data") cases = Case.create_case_map(self.get_hdfs_pipe(self.cases_path) if self.hdfs_pipe is True - else self.get_csv_file(self.cases_path), patients) + else self.get_csv_file(self.cases_path), patients, + load_limit=1000) # Load Partner data from table: LA_ISH_NGPA partners = Partner.create_partner_map(self.get_hdfs_pipe(self.partner_path) if self.hdfs_pipe is True diff --git a/spitalhygiene/vre/src/main/python/vre/model/Case.py b/spitalhygiene/vre/src/main/python/vre/model/Case.py index e93c8f3..64369e8 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Case.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Case.py @@ -167,7 +167,7 @@ def get_moves_before_dt(self, dt): return moves @staticmethod - def create_case_map(lines, patienten): + def create_case_map(lines, patienten, load_limit=None): """ Read the case csv and create Case objects from the rows. Populate a dict with cases (case_id -> case) that are not 'storniert'. Note that the function goes both ways, i.e. it adds Cases to Patients and vice versa. This function will be called by the HDFS_data_loader.patient_data() function. The lines argument corresponds to a csv.reader() instance @@ -183,6 +183,7 @@ def create_case_map(lines, patienten): :return: Dictionary mapping case ids to Case() objects --> {"0003536421" : Case(), "0003473241" : Case(), ...} """ logging.debug("create_case_map") + import_count = 0 nr_not_found = 0 nr_ok = 0 nr_not_stationary = 0 @@ -197,6 +198,9 @@ def create_case_map(lines, patienten): if patienten.get(fall.patient_id, None) is not None: patienten[fall.patient_id].add_case(fall) fall.add_patient(patienten[fall.patient_id]) + import_count += 1 + if load_limit is not None and import_count > load_limit: + break else: nr_not_found += 1 continue diff --git a/spitalhygiene/vre/src/main/python/vre/model/Medication.py b/spitalhygiene/vre/src/main/python/vre/model/Medication.py index 941881f..cc06918 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Medication.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Medication.py @@ -34,7 +34,7 @@ def __init__( def is_antibiotic(self): """Returns the antibiotic status of a Medication. - Antibiotics are identified via the prefix ``J01``. + Antibiotics are identified via the prefix ``J01`` in the ``self.drug_atc`` attribute. Returns: bool: Whether or not the medication ``self.drug_atc`` attribute starts with ``J01``. diff --git a/spitalhygiene/vre/src/main/python/vre/model/Patient.py b/spitalhygiene/vre/src/main/python/vre/model/Patient.py index 2ee1af2..2bf0072 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Patient.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Patient.py @@ -441,7 +441,7 @@ def get_location_info(self, focus_date, comparison_type='exact'): return tuple(location_moves) @staticmethod - def create_patient_dict(lines): + def create_patient_dict(lines, load_limit=None): """ Read the patient csv and create Patient objects from the rows. Populate a dict (patient_id -> patient). This function will be called by the HDFS_data_loader.patient_data() function. The lines argument corresponds to a csv.reader() instance @@ -455,10 +455,14 @@ def create_patient_dict(lines): Returns: Dictionary mapping PATIENTID to Patient() objects, i.e. {"00001383264" : Patient(), "00001383310" : Patient(), ...} """ logging.debug("create_patient_dict") + import_count = 0 patients = dict() for line in lines: patient = Patient(*line) patients[patient.patient_id] = patient + import_count += 1 + if load_limit is not None and import_count > load_limit: + break logging.info(f"{len(patients)} patients created") return patients From 250c26ce0bdb3001b37710dab5325a9893ba6e1e Mon Sep 17 00:00:00 2001 From: Stefan Zahnd Date: Fri, 2 Aug 2019 16:45:58 +0200 Subject: [PATCH 9/9] Adjusted VRE Screen data loading --- spitalhygiene/resources/Query_Atelier_Data.py | 2 +- spitalhygiene/sql/vre/LA_ISH_NBEW.sql | 1 + spitalhygiene/sql/vre/OE_PFLEGE_MAP.sql | 3 + spitalhygiene/sql/vre/V_LA_ISH_NFAL_NORM.sql | 1 + .../vre/src/main/python/vre/BasicConfig.ini | 2 +- .../src/main/python/vre/HDFS_data_loader.py | 34 ++- .../vre/src/main/python/vre/model/Case.py | 6 +- .../vre/src/main/python/vre/model/Move.py | 8 +- .../vre/src/main/python/vre/model/Patient.py | 30 ++ .../vre/src/main/python/vre/model/Risk.py | 279 ++++++++++++------ .../vre/src/main/python/vre/test2.py | 160 ---------- .../src/main/python/vre/test_data_loader.py | 22 ++ 12 files changed, 277 insertions(+), 271 deletions(-) create mode 100644 spitalhygiene/sql/vre/OE_PFLEGE_MAP.sql delete mode 100644 spitalhygiene/vre/src/main/python/vre/test2.py create mode 100644 spitalhygiene/vre/src/main/python/vre/test_data_loader.py diff --git a/spitalhygiene/resources/Query_Atelier_Data.py b/spitalhygiene/resources/Query_Atelier_Data.py index 2514ae5..fc0cb18 100644 --- a/spitalhygiene/resources/Query_Atelier_Data.py +++ b/spitalhygiene/resources/Query_Atelier_Data.py @@ -79,7 +79,7 @@ def WriteSQLToCSV(path_to_sql, path_to_csv, csv_sep, connection_file, trusted_co sql_files = [each_file for each_file in os.listdir(SQL_DIR) if each_file.endswith('.sql')] # --> Use this line instead for loading only specific files: - # sql_files = [each_file for each_file in os.listdir(SQL_DIR) if each_file in ['deleted_screenings.sql']] + # sql_files = [each_file for each_file in os.listdir(SQL_DIR) if each_file in ['OE_PFLEGE_MAP.sql']] for each_file in sql_files: print('--> Loading file: ' + each_file + ' ... ', end='', flush=True) diff --git a/spitalhygiene/sql/vre/LA_ISH_NBEW.sql b/spitalhygiene/sql/vre/LA_ISH_NBEW.sql index fb3da0a..405acc0 100644 --- a/spitalhygiene/sql/vre/LA_ISH_NBEW.sql +++ b/spitalhygiene/sql/vre/LA_ISH_NBEW.sql @@ -18,3 +18,4 @@ SELECT ,[STORN] ,[EXTKH] FROM [Atelier_DataScience].[atl].[LA_ISH_NBEW] +WHERE STORN IS NULL -- Exclude stornierte Bewegungen diff --git a/spitalhygiene/sql/vre/OE_PFLEGE_MAP.sql b/spitalhygiene/sql/vre/OE_PFLEGE_MAP.sql new file mode 100644 index 0000000..e4d1a55 --- /dev/null +++ b/spitalhygiene/sql/vre/OE_PFLEGE_MAP.sql @@ -0,0 +1,3 @@ +SELECT [oe_pflege] + ,[oe_pflege_map] +FROM [Atelier_DataScience].[dbo].[OE_PFLEGE_MAP] diff --git a/spitalhygiene/sql/vre/V_LA_ISH_NFAL_NORM.sql b/spitalhygiene/sql/vre/V_LA_ISH_NFAL_NORM.sql index 4da7d00..1db0d72 100644 --- a/spitalhygiene/sql/vre/V_LA_ISH_NFAL_NORM.sql +++ b/spitalhygiene/sql/vre/V_LA_ISH_NFAL_NORM.sql @@ -9,3 +9,4 @@ SELECT ,[PATIENTTYP] ,[PATIENTSTATUS] FROM [Atelier_DataScience].[atl].[V_LA_ISH_NFAL_NORM] +WHERE FALAR = 1 diff --git a/spitalhygiene/vre/src/main/python/vre/BasicConfig.ini b/spitalhygiene/vre/src/main/python/vre/BasicConfig.ini index 95a7382..3d7f71c 100644 --- a/spitalhygiene/vre/src/main/python/vre/BasicConfig.ini +++ b/spitalhygiene/vre/src/main/python/vre/BasicConfig.ini @@ -39,4 +39,4 @@ csv_sep = , data_basis = all # Number of patients and cases to be loaded, either None (load all data) or any positive integer -load_limit = None +load_limit = 1000 diff --git a/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py b/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py index 65b1d45..0f1721c 100644 --- a/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py +++ b/spitalhygiene/vre/src/main/python/vre/HDFS_data_loader.py @@ -71,11 +71,15 @@ def __init__(self, hdfs_pipe=True): self.icd_path = os.path.join(self.base_path, "LA_ISH_NDIA_NORM.csv") self.VRE_screenings_path = os.path.join(self.base_path, "V_VRE_SCREENING_DATA.csv") self.VRE_ward_screenings_path = os.path.join(self.base_path, "WARD_SCREENINGS.csv") + self.oe_pflege_map_path = os.path.join(self.base_path, "OE_PFLEGE_MAP.csv") self.hdfs_pipe = hdfs_pipe # binary attribute specifying whether to read data Hadoop (True) or CSV (False) self.file_delim = config_reader['DELIMITERS']['csv_sep'] # delimiter character for reading CSV files + self.load_limit = None if config_reader['PARAMETERS']['load_limit'] == 'None' \ + else int(config_reader['PARAMETERS']['load_limit']) + def get_hdfs_pipe(self, path): """Loads a datafile from HDFS. @@ -150,13 +154,13 @@ def patient_data(self, risk_only=False): logging.info("loading patient data") patients = Patient.create_patient_dict(self.get_hdfs_pipe(self.patients_path) if self.hdfs_pipe is True else self.get_csv_file(self.patients_path), - load_limit=1000) + load_limit=self.load_limit) # Load Case data from table: V_LA_ISH_NFAL_NORM logging.info("loading case data") cases = Case.create_case_map(self.get_hdfs_pipe(self.cases_path) if self.hdfs_pipe is True else self.get_csv_file(self.cases_path), patients, - load_limit=1000) + load_limit=self.load_limit) # Load Partner data from table: LA_ISH_NGPA partners = Partner.create_partner_map(self.get_hdfs_pipe(self.partner_path) if self.hdfs_pipe is True @@ -168,10 +172,26 @@ def patient_data(self, risk_only=False): # Load Move data from table: LA_ISH_NBEW logging.info("loading move data") Move.add_move_to_case(self.get_hdfs_pipe(self.moves_path) if self.hdfs_pipe is True - else self.get_csv_file(self.moves_path), cases, rooms, wards, partners) + else self.get_csv_file(self.moves_path), cases, rooms, wards, partners, + load_limit=self.load_limit) # --> Note: Move() objects are not part of the returned dictionary, they are only used in # Case() objects --> Case().moves = [1 : Move(), 2 : Move(), ...] + # Generate ward screening overview map + screen_map = Risk.generate_screening_overview_map(self.get_hdfs_pipe(self.VRE_ward_screenings_path) + if self.hdfs_pipe is True + else self.get_csv_file(self.VRE_ward_screenings_path)) + # --> this yields a dictionary mapping dt.date() objects to tuples of (ward_name, screening_type) + # i.e. of the form {'2018-10-22' : ('O SUED', 'W'), '2018-09-15' : ('IB BLAU', 'E'), ...} + + # Generate OE_pflege_map + oe_pflege_map = Risk.generate_oe_pflege_map(self.get_hdfs_pipe(self.oe_pflege_map_path) + if self.hdfs_pipe is True + else self.get_csv_file(self.oe_pflege_map_path)) + # --> yields a dictionary mapping "inofficial" ward names to official ones found in the OE_pflege_abk column + # of the dbo.INSEL_MAP table in the Atelier_DataScience. This name allows linkage to Waveware ! + # i.e. of the form {'BEWA' : 'C WEST', 'E 121' : 'E 120-21', ...} + # ---------------------------------------------------------------- # Load Risk data --> ADJUST THIS SECTION ! logging.info("loading risk data") @@ -183,9 +203,11 @@ def patient_data(self, risk_only=False): # else self.get_csv_file(self.deleted_risks_path), patients # ) # ## --> NEW VERSION: from file VRE_Screenings_Final.csv - Risk.add_screening_data_to_patients(lines=self.get_hdfs_pipe(self.VRE_screenings_path) - if self.hdfs_pipe is True - else self.get_csv_file(self.VRE_screenings_path), patient_dict=patients) + Risk.add_annotated_screening_data_to_patients(self.get_hdfs_pipe(self.VRE_screenings_path) + if self.hdfs_pipe is True + else self.get_csv_file(self.VRE_screenings_path), + patient_dict=patients) + if risk_only: logging.info("keeping only risk patients") diff --git a/spitalhygiene/vre/src/main/python/vre/model/Case.py b/spitalhygiene/vre/src/main/python/vre/model/Case.py index 64369e8..206bf56 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Case.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Case.py @@ -190,9 +190,9 @@ def create_case_map(lines, patienten, load_limit=None): cases = dict() for line in lines: fall = Case(*line) - if fall.fal_ar != '1': # exclude non-stationary cases (for stationary cases: Case().falar == '1' !) - nr_not_stationary += 1 - continue + # if fall.fal_ar != '1': # exclude non-stationary cases (for stationary cases: Case().falar == '1' !) + # nr_not_stationary += 1 # NOW INCLUDED DIRECTLY IN THE SQL QUERY + # continue if fall.case_status == "aktiv": # exclude entries where "CASESTATUS" is "storniert" cases[fall.case_id] = fall if patienten.get(fall.patient_id, None) is not None: diff --git a/spitalhygiene/vre/src/main/python/vre/model/Move.py b/spitalhygiene/vre/src/main/python/vre/model/Move.py index 0574c5d..9bbfd8a 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Move.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Move.py @@ -74,7 +74,7 @@ def create_bwart_map(lines): bwart[line[0]] = line[1] return bwart - def add_move_to_case(lines, faelle, rooms, wards, partners): + def add_move_to_case(lines, faelle, rooms, wards, partners, load_limit=None): """ Reads the moves csv and performs the following: --> creates a Move() object from the read-in line data @@ -105,8 +105,8 @@ def add_move_to_case(lines, faelle, rooms, wards, partners): else: move = Move(*line) # don't consider cancelled movements - if move.storn == "X": - continue + # if move.storn == "X": # NOW INCLUDED DIRECTLY IN THE SQL QUERY + # continue if faelle.get(move.fal_nr, None) is not None: faelle[move.fal_nr].add_move(move) @@ -150,5 +150,7 @@ def add_move_to_case(lines, faelle, rooms, wards, partners): partners[move.ext_kh].add_case(move.case) move.case.add_referrer(partners[move.ext_kh]) nr_ok += 1 + if load_limit is not None and nr_ok > load_limit: + break logging.info(f"{nr_ok} ok, {nr_not_found} cases not found, {nr_not_formatted} malformed, {nr_wards_updated} wards updated") diff --git a/spitalhygiene/vre/src/main/python/vre/model/Patient.py b/spitalhygiene/vre/src/main/python/vre/model/Patient.py index 2bf0072..c43506b 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Patient.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Patient.py @@ -440,6 +440,36 @@ def get_location_info(self, focus_date, comparison_type='exact'): return tuple(location_moves) + def get_moves_at_date(self, target_date): + """Extracts all moves from cases for this patient at ``target_date``. + + This function returns a list of all moves for this patient that took place at ``target_date``. This should + ideally be only a single entry, since a patient cannot be stationed in two or more places simultaneously. + However, a situation may arise when a patient is transferred between wards, in which case there will be two + moves matching the criteria. + + Args: + target_date (datetime.date()): Date for which moves will be extracted form this patient's cases. + + Returns: + list: List of Move() objects which have taken place for this patient at ``target_date``. + """ + candidate_moves = [] + for each_case in self.cases.values(): + # print(each_case.moves) + for each_move in each_case.moves.values(): + # print(each_move.bwe_dt) + # each_move is a dictionary of the form {1: Move(), 2: Move(),...} + move_start = each_move.bwi_dt.date() if each_move.bwi_dt is not None else None + move_end = each_move.bwe_dt.date() if each_move.bwe_dt is not None else None + if move_start is None or move_end is None: + continue + else: + if move_start <= target_date <= move_end: + candidate_moves.append(each_move) + + return candidate_moves + @staticmethod def create_patient_dict(lines, load_limit=None): """ diff --git a/spitalhygiene/vre/src/main/python/vre/model/Risk.py b/spitalhygiene/vre/src/main/python/vre/model/Risk.py index f219668..e7d6af8 100644 --- a/spitalhygiene/vre/src/main/python/vre/model/Risk.py +++ b/spitalhygiene/vre/src/main/python/vre/model/Risk.py @@ -1,55 +1,30 @@ -from datetime import datetime +import datetime import logging class Risk: - def __init__(self, auftrag_nbr, erfassung, entnahme, pat_nr, sex, PID, fall_nr, auftraggeber, kostenstelle, - material_type, transport, resultat, vreih, analyse_methode, screening_context, ward, options): + """Models a ``Risk`` (i.e. Screening) object. + """ + def __init__(self, auftrag_nr, erfassung, entnahme, vorname, nachname, gbdat, pat_nr, pruefziffer, PID, + auftraggeber, kostenstelle, material_type, transport, resultat, analyse_methode, screening_context): """Initiates a Risk (i.e. Screening) object. - - Auftrag_Nummer - - Erfassung - - Entnahme - - Patient_Number - - Geschlecht - - PID - - Fall_Nummer - - Auftraggeber - - Kostenstelle - - Material_Typ - - Transport - - Resultat - - vreih - - Analyse_Methode - - Screening_Context - - Ward - - Options - :param patient_id: - :param screen_code: - :param entry_date: - :param context: """ - self.auftrag_nbr = auftrag_nbr - self.erfassung = datetime.strptime(erfassung, '%d.%m.%Y').date() if erfassung != '' else None - self.entnahme = datetime.strptime(entnahme, '%d.%m.%Y').date() if entnahme != '' else None + self.auftrag_nbr = auftrag_nr + self.erfassung = datetime.datetime.strptime(erfassung, '%Y-%m-%d').date() if erfassung != '' else None + self.entnahme = datetime.datetime.strptime(entnahme, '%Y-%m-%d').date() if entnahme != '' else None + self.vorname = vorname + self.nachname = nachname + self.geburtsdatum = gbdat self.patient_nbr = pat_nr - self.patient_sex = sex - # Important note: adjust PID to have 11 digits, as it corresponds to [Patient_Number] + [Prüfziffer] - # --> used for matching to patient IDs in the VRE model ! - self.pid = ''.join(['0' for i in range(0, 11-len(PID))] + [PID]) - self.fall_nbr = fall_nr + self.pruefziffer = pruefziffer + self.pid = PID self.auftraggeber = auftraggeber self.kostenstelle = kostenstelle self.material_type = material_type self.transport = transport self.result = resultat - self.vreih = vreih self.analysis_method = analyse_methode self.screening_context = screening_context - self.ward = ward - self.options = options - - def old_init(self, patient_id, screen_code, entry_date, context): - print('hi') # old __init function def NOLONGERUSED_add_risk_to_patient(lines, patients): """Reads the risk csv, create Risk objects from the rows and adds these to the respective patients. @@ -107,75 +82,185 @@ def NOLONGERUSED_add_deleted_risk_to_patient(lines, patients): logging.info(f"{nr_ok} risks added, {nr_not_found} patients not found for deleted risk, {nr_malformed} malformed.") @staticmethod - def add_screening_data_to_patients(lines, patient_dict): - """Adds screening data to all patients in the model. - - Reads the custom-made VRE_Screenings_Final.csv file, which is produced in the Python Project "VRE Aggregation". - This file is created from manually extracted IFIK data, and cannot be currently extracted automatically on - Hadoop. This function will be called by the HDFS_data_loader.patient_data() function (lines is an iterator - object). The underlying file is the ONLY necessary file for VRE results, and contains an aggregation of - all IFIK screening results. Note that this file also contains an `Options` column which is used as a basis for - validating ward data, since a particular screening type may be active in multiple wards simultaneously. As a - consequence, the `Move()` data associated with all patients in the model must be used to correctly assign the - ward in which a particular screening has taken place. The raw input file contains the following columns: - - - Auftrag_Nummer - - Erfassung - - Entnahme - - Patient_Number - - Geschlecht - - PID - - Fall_Nummer - - Auftraggeber - - Kostenstelle - - Material_Typ - - Transport - - Resultat - - vreih - - Analyse_Methode - - Screening_Context - - Ward - - Options - - More details on the exact structure of the read-in file can be found in the Python Project "VRE Aggregation". - - Note that all screening results are converted into a Risk() object, validated, and added to Patient() objects - in patient_dict using the ``Patient().add_risk()`` function. + def generate_screening_overview_map(lines): + """Generates the ward screening overview dictionary. + + This function will generate a dictionary containing an overview of which screenings were active at any + particular day. This information is extracted from an iterator object (``lines``), which provides data from + the Atelier_DataScience in the table ``dbo.WARD_SCREENINGS``. The table is formatted as follows: + + ============= ======= ============== + screening_day org_pf screening_type + ============= ======= ============== + 2018-01-01 O MITTE W + 2018-01-01 O SUED E + ============= ======= ============== + + Args: + lines (iterator() object): csv iterator from which data will be read + + Returns: + dict: + Dictionary mapping ``datetime.date()`` objects to tuples of length 2 of the form + ``(ward_name, screening_type)`` + + :math:`\\longrightarrow` ``{'2018-10-22' : ('O SUED', 'W'), '2018-09-15' : ('IB BLAU', 'E'), ...}`` + """ + screening_dict = {} + for each_line in lines: + extracted_date = datetime.datetime.strptime(each_line[0], '%Y-%m-%d').date() + if extracted_date not in screening_dict: + screening_dict[extracted_date] = [(each_line[1], each_line[2])] + else: + screening_dict[extracted_date].append( (each_line[1], each_line[2]) ) + + return screening_dict + + @staticmethod + def generate_oe_pflege_map(lines): + """Generates the oe_pflege_map dictionary. + + This function will generate a dictionary mapping "inofficial" ward names to the names of official *Pflegerische + OEs*. This information is extracted from an iterator object (lines), which provides data from the + Atelier_DataScience in the table ``dbo.OE_PFLEGE_MAP``. The table is formatted as follows: + + ========= ============= + oe_pflege oe_pflege_map + ========= ============= + E 108 E120-21 + BEWA C WEST + ========= ============= + + Args: + lines (iterator() object): csv iterator from which data will be read + + Returns: + dict: + Dictionary mapping inofficial ward names to official names of *pflegerische OEs* + + :math:`\\longrightarrow` ``{'BEWA' : 'C WEST', 'E 121' : 'E 120-21', ...} `` + """ + oe_pflege_dict = {} + for each_line in lines: + oe_pflege_dict[each_line[0]] = each_line[1] + + return oe_pflege_dict + + @staticmethod + def add_annotated_screening_data_to_patients(lines, patient_dict): + """Annotates and adds screening data to all patients in the model. + + This function is the core piece for adding VRE screening data to the model. It will read all screenings exported + from the Atelier_DataScience ``V_VRE_SCREENING_DATA`` view, which is structured as follows: + + ========== ========== ========== =============== ============= ============ ========== =========== =========== ============ ============ ============ ========= ======== =============== ================= + auftrag_nr erfassung entnahme vorname nachname geburtsdatum patient_nr pruefziffer patient_id auftraggeber kostenstelle material_typ transport resultat analyse_methode screening_context + ========== ========== ========== =============== ============= ============ ========== =========== =========== ============ ============ ============ ========= ======== =============== ================= + 1234567 2018-33-33 2018-33-33 Mister X 2999-33-33 0000000000 8 00000000000 S099 sepi are tmpo nn PCR NULL + 1234567 2018-33-33 2018-33-33 Mister X 2999-33-33 0000000000 8 00000000000 sepi sepi are tmpo nn PCR NULL + 1234567 2018-33-33 2018-33-33 Misses Y 2999-33-33 0000000000 9 00000000000 sepi sepi are tmpo nn PCR Klinisch + ========== ========== ========== =============== ============= ============ ========== =========== =========== ============ ============ ============ ========= ======== =============== ================= + + A Risk() object will be created from each line received. These risk objects have the following arguments: + + - auftrag_nr + - erfassung + - entnahme + - patient_nr + - pruefziffer + - patient_id + - auftraggeber + - kostenstelle + - material_typ + - transport + - resultat + - analyse_methode + - screening_context + - ward_name + - room_name + - pflege_oe_name + + The ``screening_context`` attribute will either be set to *Klinisch* or ``NULL``. If set to *Klinisch*, this + indicates that the screening was performed as a non-ordinary VRE screening such as testing a urine sample for + the presence of VRE bacteria. These screenings will be added to the VRE model without further processing. The + same is true for VRE screening entries set to *Ausland*, which refer to patients having been transpored from + abroad to Switzerland and being screened here upon hospital entry. + + For most screenings however, the ``screening_context`` will be set to NULL. In this case, the context must be + **extracted** from the data available in the model. This process is tedious and involves the following steps: + + 1) Extract Move() (in german: *Aufenthalt*) objects for screening which were available to the patient at the + time of ``erfassung``. This **must** be a single move, as a patient cannot be stationed in two different + wards simultaneously. + 2) Extract the Ward() for the Move() extracted in step 1, and add the ward name to the ``self.ward_name`` + attribute. + 3) Extract the exact Room() name where the patient was located from the Move(), and add it to the + ``self.room_name`` attribute. This room name is sometimes indicated for Moves(), but will not be available + for the vast majority of data. In that case, room_name will be set to ``None`` instead. + 4) Map the extracted Ward() name to an official *Pflegerische OE* using the ``oe_pflege_map`` dictionary. This + dictionary maps "inofficial" ward names to an official name of a *pflegerische OE*. This step is very + important, as the official name of the *pflegerische OE* allows the creation of a link to Waveware and + thereby the correct floor, building, and room (in some situations) in which the screening has taken place. + The officially extracted pflegerische OE will be appended to the ``self.pflege_oe_name`` attribute. + 5) Check which type of screening was active in the *pflegerische OE* extracted in step 4. This information is + found in the ``ward_screening_overview`` dictionary, which indicates the various types of screenings that + were active at specific dates in different pflegerische OEs. Note that in this step, if no screening + context can be found for a particular VRE screen at its date of ``erfassung``, the dates in + ``ward_screening_overview`` will be matched with a tolerance of **+/- 1 day** due to the fact that VRE + screenings are occasionally performed one day sooner or later than planned. Args: lines (iterator): iterator object of the to-be-read file `not` containing the header line patient_dict (dict): Dictionary mapping patient ids to Patient() --> {'00008301433' : Patient(), ... } """ - move_wards = [] - screening_wards = [] - logging.debug("adding_all_screenings_to_patients") - nr_pat_not_found = 0 - nr_ok = 0 - for line in lines: - this_risk = Risk(*line) - if patient_dict.get(this_risk.pid) is None: # Check whether or not PID exists - nr_pat_not_found += 1 - continue - potential_moves = patient_dict.get(this_risk.pid).get_location_info(focus_date=this_risk.erfassung) - if len(potential_moves) > 0: # indicates at least one potential match - move_wards.append('+'.join([each_move.org_fa for each_move in potential_moves])) - screening_wards.append(this_risk.options) - nr_ok += 1 - # print results to file - with open('match_results.txt', 'w') as writefile: - for i in range(len(move_wards)): - writefile.write(f"{move_wards[i]}; {screening_wards[i]}\n") + load_count = 0 + for each_line in lines: + this_risk = Risk(*each_line) + # Check if this patient's PID exists in patient_dict + if this_risk.pid in patient_dict: + load_count += 1 + potential_moves = patient_dict[this_risk.pid].get_moves_at_date(this_risk.erfassung) + print(potential_moves) + # if load_count > 5: + # break - # patient_dict[this_risk.patient_id].add_risk(this_risk) - # nr_ok += 1 - # else: - # nr_pat_not_found += 1 - # continue - logging.info(f"{nr_ok} screenings added, {nr_pat_not_found} patients from VRE screening data not found.") + # + # + # + # move_wards = [] + # screening_wards = [] + # logging.debug("adding_all_screenings_to_patients") + # nr_pat_not_found = 0 + # nr_ok = 0 + # for line in lines: + # this_risk = Risk(*line) + # if patient_dict.get(this_risk.pid) is None: # Check whether or not PID exists + # nr_pat_not_found += 1 + # continue + # potential_moves = patient_dict.get(this_risk.pid).get_location_info(focus_date=this_risk.erfassung) + # if len(potential_moves) > 0: # indicates at least one potential match + # move_wards.append('+'.join([each_move.org_fa for each_move in potential_moves])) + # screening_wards.append(this_risk.options) + # nr_ok += 1 + # # print results to file + # with open('match_results.txt', 'w') as writefile: + # for i in range(len(move_wards)): + # writefile.write(f"{move_wards[i]}; {screening_wards[i]}\n") + # + # + # + # + # + # # patient_dict[this_risk.patient_id].add_risk(this_risk) + # # nr_ok += 1 + # # else: + # # nr_pat_not_found += 1 + # # continue + # logging.info(f"{nr_ok} screenings added, {nr_pat_not_found} patients from VRE screening data not found.") diff --git a/spitalhygiene/vre/src/main/python/vre/test2.py b/spitalhygiene/vre/src/main/python/vre/test2.py deleted file mode 100644 index 5338b91..0000000 --- a/spitalhygiene/vre/src/main/python/vre/test2.py +++ /dev/null @@ -1,160 +0,0 @@ -import calendar -import networkx as nx -import logging -import itertools -from collections import Counter -import os - - -def update_shortest_path_statistics(graph, focus_nodes=None): - """ Prerequisite function for calculating betweenness centrality. - - Adds new attributes to all nodes in focus_nodes, where each attribute - is a pair of nodes (sorted alphabetically) with a "SP-" prefix to tuples - of length 2 containing - (shortest_paths_through_this_node, total_shortest_paths) - For example: - { 'SP-Node1-Node2': (2, 5) , 'SP-Node1-Node3': ... } } - - Note that this may result in a lot of additional attributes for nodes - which are integral to the network. This approach is chosen because of - the fact that the networkx module does not allow updates to a - dict-of-a-dict type of attributes (i.e. if these attributes were to - be combined in a 1st-level key 'shortest-paths', the entire content - would have to be copied every time a new node-pair attribute is added, - which would make the function extremely inefficient. - - This is an important prerequisite function for the calculation of - betweenness centrality. - - Args: - focus_nodes (list): list of node IDs. If set to None (the default), - all nodes in the network will be considered. - WARNING: this may be ressource-intensive ! - """ - target_nodes = focus_nodes if focus_nodes is not None else graph.nodes - node_combinations = list(itertools.combinations(target_nodes, 2)) - # Returns a list of tuples containing all unique pairs of nodes - # in considered_nodes - - logging.info(f'--> Adding shortest path statistics considering ' - f'{len(target_nodes)} nodes yielding ' - f'{len(node_combinations)} combinations') - for count, combo_tuple in enumerate(node_combinations): - if count % 100 == 0: - logging.info(f" <> Processed {count} combinations") - all_shortest_paths = list(nx.all_shortest_paths(graph, - source=combo_tuple[0], - target=combo_tuple[1])) # Yields an iterator of lists of shortest paths for the given combination of nodes - involved_nodes = [node for sublist in all_shortest_paths - for node in - sublist] # Contains all nodes involved in all combinations of shortest paths for the given combination of nodes - node_counts = Counter(involved_nodes) - sorted_pair = sorted([combo_tuple[0], combo_tuple[1]]) - update_attr_dict = { - each_key: {'SP-' + sorted_pair[0] + '-' + sorted_pair[1]: - (node_counts[each_key], len(all_shortest_paths))} - for each_key in node_counts - if each_key not in sorted_pair} - # Then update all involved nodes in the network - nx.set_node_attributes(graph, update_attr_dict) - # Write it all to log - logging.info(f"Successfully added betweenness statistics to the network !") - - -def export_node_betweenness(graph, data_dir, csv_sep=';'): - """ Function to export node betweenness. - - This function will export node betweenness for all nodes from the - network. This is done by exporting the sum of all fractions of shortest - paths a particular node is in, which is found in the attribute keys - starting with the "SP-" prefix (e.g. "SP-Node1-Node4"). These tuples - contain 2 entries, the first being the fraction of shortest path this - node is found in (between the given pair of nodes), and the second one - being the total number of shortest paths. This function can only be - executed once the function update_shortest_path_statistics() has been - called. Nodes without an attribute key starting with the "SP-" prefix - will have node betweenness of 0. - - The exported file is written into self.data_dir and contains 3 columns: - - Node ID - - Node Type - - Betweenness Score - - Args: - csv_sep (str): separator to be used in export file. - """ - # if self.shortest_path_stats is False: - # logging.error(f"This function requires specific node attributes " - # f"which can be added with the function " - # f"update_shortest_path_statitics()") - # raise self.NodeBetweennessException('Missing a required call to' - # 'update_shortest_path_statitics()') - with open(os.path.join(data_dir, - '_node_bwn.txt'), 'w') as outfile: - outfile.write(csv_sep.join(['Node ID', 'Node Type', - 'Betweenness_Score']) + '\n') - write_counter = 0 - for node_tuple in graph.nodes(data=True): - # Returns tuples of length 2 --> (node_id, attribute_dict) - write_string = [node_tuple[0], 'hello'] - target_keys = [each_key for each_key in node_tuple[1].keys() - if each_key.startswith('SP-')] - print(target_keys) - betweenness_score = sum([node_tuple[1][each_key][0] / node_tuple[1][each_key][1] - for each_key in target_keys]) - write_string.append(str(betweenness_score)) - # Then write to outfile - outfile.write(csv_sep.join(write_string) + '\n') - write_counter += 1 - # Log progress - logging.info(f"Successfully wrote betweenness scores for " - f"{write_counter} nodes") -####################################### - -gra = nx.MultiGraph() - - -gra.add_edge('B', 'A') -gra.add_edge('F', 'A') -gra.add_edge('E', 'A') -gra.add_edge('D', 'A') - -gra.add_edge('F', 'C') -gra.add_edge('B', 'C') -gra.add_edge('D', 'B') -gra.add_edge('G', 'J') - - -print(nx.shortest_path(gra, 'B', 'F')) -print(nx.shortest_path(gra, 'B', 'F')) -print(nx.shortest_path(gra, 'B', 'F')) -print(nx.shortest_path(gra, 'B', 'F')) - -update_shortest_path_statistics(gra, focus_nodes=['B', 'C', 'E']) - -export_node_betweenness(gra, data_dir='.') - -print([node for node in gra.nodes(data=True)]) - -sys.exit() - -# gra.add_edge('P1', 'X') - -# print(nx.shortest_path(gra, 'P1', 'P3')) -# -# print(nx.is_connected(gra)) -# -# sys.exit() - -gra.add_edge('E', 'P2') -gra.add_edge('P1', 'P2') - -print(calculate_node_betweenness(gra)) -print(calculate_node_betweenness(gra, considered_nodes=['P1', 'P3'])) - - -print(calendar.monthrange(2018, 1)) - - -print('hello') if True == True else '' diff --git a/spitalhygiene/vre/src/main/python/vre/test_data_loader.py b/spitalhygiene/vre/src/main/python/vre/test_data_loader.py new file mode 100644 index 0000000..3a68234 --- /dev/null +++ b/spitalhygiene/vre/src/main/python/vre/test_data_loader.py @@ -0,0 +1,22 @@ +# Small script to test the patient import defined in the HDFS_data_loader.py script + +from HDFS_data_loader import HDFS_data_loader +from feature_extractor import feature_extractor +from networkx_graph import surface_model, create_model_snapshots +import logging +import os +import datetime +import configparser +import calendar +import pathlib + +config_reader = configparser.ConfigParser() + +config_reader.read(pathlib.Path('BasicConfig.ini')) + +logging.basicConfig(format='%(asctime)s - %(levelname)s: %(message)s', level=logging.INFO, datefmt='%d.%m.%Y %H:%M:%S') + +loader = HDFS_data_loader(hdfs_pipe=False) # hdfs_pipe = False --> files will be loaded directly from CSV +patient_data = loader.patient_data() + +

     
    + a
    + Appointment +
     
    + b
    + Bed +
     
    + c
    + Care +
    + Chop +
     
    d
    data_compiler
    + Device +
     
    + e
    + Employee +
     
    f
     
    + i
    + ICD +
     
    + m
    + Medication +
     
    n
     
    + p
    + preprocessor +
     
    q