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create mode 100644 xgboost/non_imputed/4_xgb_nonimputed_calibrated_plots.ipynb create mode 100644 xgboost/non_imputed/5_fairness_assess_get_data_xgboost.ipynb create mode 100644 xgboost/non_imputed/6_xgb_nonimputed_fairness.ipynb create mode 100644 xgboost/non_imputed/7_xgb_nonimputed_risk_categories.ipynb create mode 100644 xgboost/non_imputed/__init__.py create mode 100644 xgboost/numeric_columns.pickle diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..1235d9e --- /dev/null +++ b/.gitignore @@ -0,0 +1,171 @@ +# 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/ +pip-wheel-metadata/ +share/python-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 +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# 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 + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# 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/ + +data/ +.DS_Store +# History files +.Rhistory +.Rapp.history + +# Session Data files +.RData + +# User-specific files +.Ruserdata + +# Example code in package build process +*-Ex.R + +# Output files from R CMD build +/*.tar.gz + +# Output files from R CMD check +/*.Rcheck/ + +# RStudio files +.Rproj.user/ + +# produced vignettes +vignettes/*.html +vignettes/*.pdf + +# OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 +.httr-oauth + +# knitr and R markdown default cache directories +*_cache/ +/cache/ + +# Temporary files created by R markdown +*.utf8.md +*.knit.md + +# R Environment Variables +.Renviron diff --git a/README.md b/README.md index d01a981..6f5387f 100644 --- a/README.md +++ b/README.md @@ -1 +1,7473 @@ -# 2021PCOR-ML-AI +# Training Data for Machine Learning to Enhance PCOR Data Infrastructure + +[https://www.healthit.gov/topic/scientific-initiatives/pcor/machine-learning](https://www.healthit.gov/topic/scientific-initiatives/pcor/machine-learning) + +## Project Background +Innovative artificial intelligence (AI) methods and the increase in computational power support the use of tools and advanced technologies such as machine learning, which consumes large amounts of data to make predictions for actionable information. + +Current AI workflows make it possible to conduct complex studies and uncover deeper insights than traditional analytical methods do. As the volume and availability of electronic health data increases, patient-centered outcomes research (PCOR) investigators need better tools to analyze data and interpret those outcomes. A foundation of high-quality training data is critical to developing robust machine-learning models. Training data sets are essential to train prediction models that use machine learning algorithms, to extract features most relevant to specified research goals, and to reveal meaningful associations. + +See this [blog post](https://www.healthit.gov/buzz-blog/health-it/the-application-of-machine-learning-to-address-kidney-disease) for more background. + +Please contact onc.request@hhs.gov with questions about this project and rankin_summer@bah.com with any issues or questions about the code. + +Please see the detailed implementation guidance provided below or download it as a [pdf](./README.pdf). + + +# Implementation Guidance + +- [6.1 Data Understanding](#61-data-understanding) +- [6.2 Overall Training Dataset](#62-overall-training-dataset) + - [6.2.0 Deidentify the Data](#620-deidentify-the-data) + - [6.2.1 Overview of Cohort Creation](#621-overview-of-cohort-creation) + - [6.2.2 Connect to Postgres Database](#622-connect-to-postgres-database) + - [6.2.3 Convert Data to CSV](#623-convert-data-to-csv) + - [6.2.4 Create Patients Table](#624-create-patients-table) + - [6.2.5 Create Medevid Table](#625-create-medevid-table) + - [6.2.6 Join Patients to Medevid](#626-join-patients-to-medevid) + - [6.2.7 Create Transplant Waitlist Features](#627-create-transplant-waitlist-features) + - [6.2.8 Create Partition Data](#628-create-partition-data) + - [6.2.9 Join patients_medevid_waitlist Table to the Partition Index](#629-join-patients_medevid_waitlist-table-to-the-partition-index) + - [6.2.9.1 Calculate Demographic Subtotals Per Partition](#6291-calculate-demographic-subtotals-per-partition) + - [6.2.10 Get Pre-ESRD Claims Data](#6210-get-pre-esrd-claims-data) + - [6.2.11 Create Claims Tables](#6211-create-claims-tables) + - [6.2.12 Map Diagnosis Codes (drg_cd) to Primary Diagnosis Codes (pdgns_cd)](#6212-map-diagnosis-codes-drg_cd-to-primary-diagnosis-codes-pdgns_cd) + - [6.2.13 Get pre-2011 pre-ESRD Claims Data](#6213-get-pre-2011-pre-ESRD-claims-data) + - [6.2.14 Diagnosis Groupings](#6214-diagnosis-groupings) + - [6.2.15 Aggregate pre-ESRD Claims Data](#6215-aggregate-pre-esrd-claims-data) + - [6.2.16 Join the preesrdfeatures Tables to the Partition Index](#6216-join-the-preesrdfeatures-tables-to-the-partition-index) + - [6.2.17 Map ICD-9 to ICD-10](#6217-map-icd-9-to-icd-10) + - [6.2.18 Prepare Data for Modeling](#6218-prepare-data-for-modeling) + - [6.2.19 Impute Missing Values](#6219-impute-missing-values) + - [6.2.20 Utility Files](#6220-utility-files) + - [dx_mappings_ucsf.csv](#dx_mappings_ucsfcsv) + - [2017_I9gem_map.txt](#2017_I9gem_maptxt) + - [icd10_ccs_codes.R](#icd10_ccs_codesr) + - [icd10_dx_codes.txt](#icd10_dx_codestxt) + - [icd9_ccs_codes.R](#icd9_ccs_codesr) + - [icd9_dx_2014.txt](#icd9_dx_2014txt) + - [imputation_rules.xlsx](#imputation_rulesxlsx) + - [pre_esrd_ip_claim_variables.R](#pre_esrd_ip_claim_variablesr) + - [pre_esrd_hh_claim_variables.R](#pre_esrd_hh_claim_variablesr) + - [pre_esrd_hs_claim_variables.R](#pre_esrd_hs_claim_variablesr) + - [pre_esrd_op_claim_variables.R](#pre_esrd_op_claim_variablesr) + - [pre_esrd_sn_claim_variables.R](#pre_esrd_sn_claim_variablesr) + - [pre_esrd_pre2011_claim_variables.R](#pre_esrd_pre2011_claim_variablesr) + - [setfieldtypes.R](#setfieldtypesr) + - [6.2.21 Documentation of the Training Dataset](#6221-documentation-of-the-training-dataset) +- [6.3 ML Modeling and Evaluation](#63-ml-modeling-and-evaluation) + - [6.3.1 Non-Imputed XGBoost Model](#631-non-imputed-xgboost-model) + - [6.3.1.1 Pre-processing the training dataset](#6311-pre-processing-the-training-dataset) + - [6.3.1.2 Hyperparameter tuning for the non-imputed dataset](#6312-hyperparameter-tuning-for-the-non-imputed-dataset) + - [6.3.1.3 Final XGBoost model for the non-imputed dataset](#6313-final-xgboost-model-for-the-non-imputed-dataset) + - [6.3.1.4 Calibration](#6314-calibration) + - [6.3.1.5 Plotting calibrated results](#6315-plotting-calibrated-results) + - [6.3.1.6 Saving data for the fairness assessment](#6316-saving-data-for-the-fairness-assessment) + - [6.3.1.7 Fairness assessment](#6317-fairness-assessment) + - [6.3.1.8 Risk assessment](#6318-risk-assessment) + - [6.3.2 Imputed XGBoost Model](#632-imputed-xgboost-model) + - [6.3.2.1 Pre-processing the training dataset](#6321-pre-processing-the-training-dataset) + - [6.3.2.2 Hyperparameter tuning for each imputed dataset](#6322-hyperparameter-tuning-for-each-imputed-dataset) + - [6.3.2.3 Pooled hyperparameter tuning](#6323-pooled-hyperparameter-tuning) + - [6.3.2.4 Final imputed XGBoost model](#6324-final-imputed-xgboost-model) + - [6.3.2.5 Calibration](#6325-calibration) + - [6.3.2.6 Plotting calibrated results](#6326-plotting-calibrated-results) + - [6.3.2.7 Saving data for the fairness assessment](#6327-saving-data-for-the-fairness-assessment) + - [6.3.2.8 Fairness assessment](#6328-fairness-assessment) + - [6.3.2.9 Risk assessment](#6329-risk-assessment) + - [6.3.3 Logistic Regression (LR) Model](#633-logistic-regression-lr-model) + - [6.3.3.1 Pre-processing the training dataset](#6331-pre-processing-the-training-dataset) + - [6.3.3.2 Hyperparameter tuning and final logistic regression model](#6332-hyperparameter-tuning-and-final-logistic-regression-model) + - [6.3.3.3 Pool results](#6333-pool-results) + - [6.3.3.4 Plot results](#6334-plot-results) + - [6.3.3.5 Feature importance](#6335-feature-importance) + - [6.3.3.6 Fairness assessment](#6336-fairness-assessment) + - [6.3.3.7 Risk assessment](#6337-risk-assessment) + - [6.3.4 Artificial Neural Network--Multilayer Perceptron (MLP) Model](#634-artificial-neural-network--multilayer-perceptron-mlp-model) + - [6.3.4.1 Run docker container (optional)](#6341-run-docker-container-optional) + - [6.3.4.2 Run on a server (i.e. AWS)](#6342-run-on-a-server-ie-aws) + - [6.3.4.3 Pre-processing the data](#6343-pre-processing-the-data) + - [6.3.4.4 Hyperparameter tuning](#6344-hyperparameter-tuning) + - [6.3.4.5 Building layers and compiling the model](#6345-building-layers-and-compiling-the-model) + - [6.3.4.6 Final MLP model](#6346-final-mlp-model) + - [6.3.4.7 Pool results](#6347-pool-results) + - [6.3.4.8 Plot results](#6348-plot-results) + - [6.3.4.9 Fairness assessment](#6349-fairness-assessment) + - [6.3.4.10 Risk assessment](#63410-risk-assessment) +# 6.1 Data Understanding + +The source data for building the overall training dataset was obtained from the United States Renal Data System (USRDS), the national data registry developed from resources initiated by the Centers for Medicare & Medicaid Services (CMS) and its funded end-stage kidney disease (ESKD) networks and subsequently maintained by the National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK). USRDS stores and distributes data on the outcomes and treatments of chronic kidney disease (CKD) and ESKD population in the U.S. +*(Note: to be consistent with USRDS terminology for data tables, this document uses end stage renal disease - ESRD - instead of ESKD.)* To better understand the data, data profiling was performed on the demographic variables and the outcome variable of interest (mortality in the first 90 days of dialysis). +Information on constructing the outcome variable can be found in [Section 6.2.4 Create Patients Table](#624-create-patients-table). + +The distribution of patients in the cohort who survived versus died in the first 90 days after dialysis initiation: +![died_vs_survived](./images/20210220_died_vs_survived_cohort.png) + +The age distribution of patients who survived versus died in the first 90 days after dialysis initiation: +![age_boxplot](./images/20210220_age_boxplot.png) + +The sex distribution of patients who survived versus died in the first 90 days after dialysis initiation: +![sex_cohort](./images/20210220_sex_cohort.png) + +The distribution by race of patients who survived versus died in the first 90 days after dialysis initiation: +![race_cohort](./images/survive_race_cohort.PNG) + + +# 6.2 Overall Training Dataset + +Section 6.2 details the methodology used to create the overall training dataset. A high level overview of the tables used for the training dataset can be found in [Section 6.2.1 Overview of Cohort Creation](#621-overview-of-cohort-creation) and results in a final dataset with 1,150,195 observations and 188 variables. The final dataset used for modeling is stored in PostgreSQL (Postgres) tables called `medxpreesrd`for the non-imputed variables and `micecomplete_pmm`for the imputed variables (5 sets of imputations were generated; more information on imputations can be found in [Section 6.2.19 Impute Missing Values](#6219-impute-missing-values)). + +The construction of `medxpreesrd` involves using more than 20 USRDS data tables, as well as publicly available data, for mapping diagnosis codes to groupings. + +All scripts are located in the DataSet/ directory on GitHub. + +Two types of files are involved in constructing `medxpreesrd`: +1) Sequential scripts - these have the prefix **S0-, "S1-"**, etc. to indicate the sequence in which they are run +2) Utility scripts - these create the data used by the sequential scripts + +Other resources that could be helpful to users include: +- [USRDS Researcher's Guide](https://www.usrds.org/media/2211/usrds_res_guide_app_b_datafiledescrip_16.pdf) +- [USRDS Researcher's Guide Appendix A](https://www.usrds.org/media/2457/usrds_res_guide_appendix_a-2020.pdf) +- [USRDS Researcher's Guide Appendix B](https://www.usrds.org/media/2458/usrds_res_guide_appendix_b_2020.pdf) +- [USRDS Researcher's Guide Appendix C](https://www.usrds.org/media/2459/usrds_res_guide_appendix_c_2020.pdf) +- [USRDS Researcher's Guide Appendix D](https://www.usrds.org/media/2460/usrds_res_guide_appendix_d_2020.pdf) + +## 6.2.0 Deidentify the Data + +The data received from USRDS was de-identified before use to comply with the approved University of San Francisco (UCSF) institutional review board (IRB) study plan. As per the Health Insurance Portability and Accountability Act (HIPAA) guidance, the following are identifiers: +- All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, date of death +- All ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older +- All geographic subdivisions smaller than a state, including street address, city, county, precinct, zip code, and their equivalent geocodes + +The date variables in USRDS were de-identified by offsetting each date by a randomly chosen number specific to each patient. For example, if first ESRD service date is April 5, 2016 and the random offset is 60 days, then first ESRD service day is transformed to April 5, 2016 plus 60 days (or June 5, 2016); for the same patient, if the date of birth is Sept 1, 1950, then date of birth gets transformed to Sept 1, 1950 plus 60 days (or Nov 1, 1950). +The ages of patients were de-identified by setting the age of all patients over the age of 90 to 90. +The location variables were de-identified by removing all location variables (zip code, etc.) from the dataset. + +***Points to consider*** + +1. Other methods can be used to de-identify locations without completely deleting the variables, such as by combining all zip codes with the same three initial digits to form geographic units containing more than 20,000 people according to the current publicly available data from the Bureau of the Census. For all such geographic units containing 20,000 for fewer people, the initial three digits should be changed to 000. + +2. Complete de-identification of the datasets obtained from USRDS was performed to comply with UCSF IRB requirements. Not all IRBs may require that PII/PHI be de-identified prior to use in a project. Future researchers may consider working with their IRB to ensure that relevant identifier variables for a specific use case are retained in the source dataset used for building the training datasets and ML models. + +## 6.2.1 Overview of Cohort Creation + +This diagram is a high level view of the tables used to create the cohort for the dataset. The number of rows and/or patients is listed at each stage of the cohort selection. Each of these R scripts is detailed below in the guide. The colors represent the following items: +- Yellow = R scripts +- Pink = Tables in the PostgreSQL database +- Green = Inclusion criteria + +![flow_of_data](./images/ONC_ML_Dataset_Flowchart.svg) + +## 6.2.2 Connect to Postgres Database + +***Steps for running the S0-connectToPostgres.R script*** + +This script creates and calls a function to 1) create a connection to a Postgres database and 2) drop a Postgres table if it exists. Both of these functions are used heavily throughout the dataset creation and are typically loaded into the top of each script. Each user will have their own details for connecting to Postgres. + +**Environment**: + +The Postgres Database for this project was hosted in an Amazon Web Services (AWS) environment with the following specifications: +``` +Name: t2.large +vCPU: 2 +GPU: 0 +Architecture: x86_64 +Memory: 8 GB +Storage: 500 GB +Operating System: Windows +Network Performance: low to moderate +Zone: US govcloud west +``` +The overall training dataset set was created using R (version 3.6.3 (2020-02-29) running on x86_64 Linux Ubuntu 20.04.1 LTS) and a PostgreSQL database (PostgreSQL 12.3, compiled by gcc (GCC) 4.8.3 20140911 (Red Hat 4.8.3-9), 64-bit). The specific R libraries and versions are shown in the table below: + +|R library |Version | +|-------------------|---------| +|RPostgres | 1.3.1| +|DBI |1.1.1| +|stringr| 1.4.0| +|haven |2.4.0| +|readr |1.4.0| +|lubridate| 1.7.9.2| +|dplyr |1.0.4| +|magrittr| 1.5| +|tidyr |1.1.2| +|sqldf |0.4-11| +|RSQLite| 2.2.3| +|gsubfn |0.7| +|proto |1.0.0| +|readxl |1.3.1| +|plyr |1.8.6| +|mice |3.13.0| + +**Input**: +``` +Name of the database +database port +user name +user password +``` + +Step 1. Create function for connecting to Postgres. +``` +dbConnect( + RPostgres::Postgres(), + dbname = cred$dbname, + host=cred$host, + port=cred$port, + user=cred$user, + password=cred$password + ) +- Output: + - An object called "con" that can be used in database queries. +- Example: +con = getConnection() +``` +Step 2. Create function for dropping a Postgres table if it exists. +``` +drop_table_function <- function(con, tablename) { + if (isTRUE(dbExistsTable(con, tablename))) { + print(str_glue("existing {tablename} table dropped")) + dbRemoveTable(con, tablename) + } + else { + print(str_glue("{tablename} table does not exist")) + } +} +``` +## 6.2.3 Convert Data to CSV + +***Steps for running the S1a_convertSAStoCSV.R script*** + +This script reads in a list of files from the raw source data (**.sas7bdat** files in USRDS data) and saves them as a .csv file. The reason for this conversion to CSV before loading into R is that the SAS files contain more information than is needed/able to store in Postgres or an R table, such as categorical variable encodings that are also documents in the USRDS Researchers Guide Appendix B and C. + +**Input**: +- source_data_dir string path to the raw data file +- file_name string name of the file without the extension +- output_data_dir string path to the output directory for csv files + +**Ouput**: +- A .csv version of the file with the same file_name + +Step 1. Convert and combine the raw source data from .sas7bdat files to .csv files +``` +convert_to_csv = function(source_data_dir, file_name, output_data_dir) { + raw_file_path = haven::read_sas(str_glue("{source_data_dir}{file_name}.sas7bdat")) + csv_path = str_glue("{output_data_dir}{file_name}.csv") + write_csv(raw_file_path, csv_path) +} +``` +- Example: +``` +convert_to_csv("/home/sas_data_usrds/", "patients", "/home/csv_data_usrds/")} +``` + +## 6.2.4 Create Patients Table +***Steps for running the S1b_patients.R script*** + +This script creates the table `patients`in the Postgres database, after filtering on the criteria to create the study cohort and the dependent variable (died in the first 90 days of dialysis). + +**Input**: csv files are produced in script [S1a-convertSAStoCSV.R](#623-convert-data-to-csv) +``` +patients.csv +``` +**Output**: Postgres table +``` +patients +``` + +Step 1. Import patients and apply exclusion criteria +``` +cohort_patients = read_csv(file.path(data_dir, "patients.csv"), col_types = cols( + CDTYPE = "c")) + +names(cohort_patients) = tolower(names(cohort_patients)) +``` +Connect to the Postgres database using the [S0-connectToPostgres.R](#622-connect-to-postgres-database) script, which results in the variable **con** that is used in the queries to Postgres. This also imports the **drop_table_function** used when creating a Postgres table. These functions are used in almost every script and will be imported at the top in the code. +``` +source(file.path(source_dir, "S0-connectToPostgres.R")) +``` +Store the raw cohort_patients data as "patients" table in Postgres (after dropping the table if it exists). +``` +fields = names(cohort_patients) +drop_table_function(con, "patients") + dbCreateTable( + con, + name = "patients", + fields = cohort_patients, + row.names = NULL + ) +dbWriteTable( + con, + name = "patients", + value = cohort_patients, + row.names = FALSE, + append = TRUE + ) +``` +The `patients` table holds the data for the 1,150,195 patients (rows) in the cohort which is created by excluding the following rows: +- age less than 18 +- incident year before 2008 +- incident year after 2017 +- first dialysis date is missing +- death date is before first dialysis date (one patient) + +The script does each exclusion criteria separately to calculate the number of rows/patients at each stage, but the exclusion can be done more simply with one SQL query shown below (and commented out in the script). +``` +exclude_patients = str_glue( + "DELETE FROM patients + WHERE inc_age<18 + OR incyear>2017 + OR incyear<2008 + OR masked_firstdial IS NULL + OR masked_died 90 are set to 90. +``` +patients_dependent_var = cohort_patients %>% + mutate(inc_age=ifelse(inc_age>90, 90, inc_age), +``` +Step 3. Create the **days_on_dial** variable by converting the **masked_died** and **masked_firstdial** to a date and by calculating the number of days between them. +``` + masked_firstdial = as_date(masked_firstdial, origin = "1960-01-01"), + masked_died = as_date(masked_died, origin = "1960-01-01"), + days_on_dial = as.double(difftime(masked_died, + masked_firstdial, + units = "days")), +``` +Step 4. Create the dependent variable (outcome variable) **died_in_90** by setting all patients with a value for **days_on_dial** to 1 (died) and patients with no value for **days_on_dial** to 0 (survived). +``` + died_in_90 = ifelse(is.na(days_on_dial), 0, ifelse(days_on_dial <= 90, 1, 0)), +``` +Step 5. Convert data variables to dates that are used to calculate waitlist and transplant status. +``` + masked_first_se = as_date(masked_first_se, origin = "1960-01-01"), +``` +**can_first_listing_dt** is the first date patient is ever waitlisted. +``` + masked_can_first_listing_dt = as_date(masked_can_first_listing_dt, origin = "1960-01-01"), +``` +**can_rem_dt** is the date patient was removed from the waitlist the first time. +``` + masked_can_rem_dt = as_date(masked_can_rem_dt, origin = "1960-01-01"), + masked_tx1date = as_date(masked_tx1date, origin = "1960-01-01"), + masked_tx1fail = as_date(masked_tx1fail, origin = "1960-01-01") +``` +Step 6. Save this table to Postgres as `patients` after dropping the initial `patients` table. +``` +drop_table_function(con, "patients") + dbCreateTable( + con, + name = "patients", + fields = patients_dependent_var, + row.names = NULL + ) + dbWriteTable( + con, + name = "patients", + value = patients_dependent_var, + row.names = FALSE, + append = TRUE + ) +``` +***Points to consider*** + +1. The study cohort as well as the outcome variable for the use case should be driven by a strong clinical understanding of the data and defined with clinician input. For example, kidney transplant events occurring within the first 90 days of initiating dialysis were evaluated as a potential competing outcome for death. However, since only less than 1% of the patient cohort recieved kidney transplants, retaining the patients in the study cohort would likely have only a small effect on modeling. After consultating with clinicans and the Technical Expert Panel, it was determined that patients with kidney transplants should be retained in the patient cohort for the purposes of this analysis. + +2. The origin date when converting data variables to readable dates is 1960-01-01. + +## 6.2.5 Create Medevid Table +The Medevid table contains the data from [CMS Form 2728 -- Medical Evidence Form](https://www.cms.gov/Medicare/CMS-Forms/CMS-Forms/Downloads/CMS2728.pdf), a form required to be completed and submitted when a patient is diagnosed with ESRD and receives their first chronic dialysis treatment(s) or receives a transplant. Medevid contains data on patient demographics, insurance status, comorbid conditions, primary cause of kidney failure, and laboratory values at the time of ESRD diagnosis as well as prior nephrology care, dietician care, and patient education. + +***Steps to running the 4.1.5 S1c_medevid.R script*** + +This script creates the `medevid` table in Postgres database. +The `medevid` table is filtered based on the following: +- Keep only rows with a matching **usrds_id** from our `patients` table +- For **usrds_ids** with multiple entries, select the first entry (in the table order) for each **usrds_id** +- The final table results in 1,150,195 patients/rows + +**Input**: csv files are produced in script [S1a-convertSAStoCSV.R](#623-convert-data-to-csv) +``` +patients +medevid.csv +``` +**Output**: Postgres table +``` +medevid +``` +Step 1. Import `medevid` data +``` +raw_medevid = read_csv(file.path(data_dir, filename), col_types = cols( + CDTYPE = "c", + masked_UREADT = "c", + ALGCON = "c", + PATNOTINFORMEDREASON = "c", + RACEC = "c", + RACE_SUB_CODE = "c")) + + names(raw_medevid) = tolower(names(raw_medevid)) +``` +Step 2. Import the **usrds_ids** from the `patients` table +``` +patients_filtered = dbGetQuery( + con, + str_glue( + " + SELECT * + FROM {table_name_pt} + ")) +``` +Step 3. Remove unused columns + +Columns with the comorbidities that were only collected in the 1995 version of the Medical Evidence Form were removed from the training dataset as patients with ESRD incident years between 2008-2017 do not have this data collected. +``` +medevid_ids_filtered = raw_medevid %>% + select(-c( + "como_ihd", + "como_mi", + "como_cararr", + "como_dysrhyt", + "como_pericar", + "como_diabprim", + "como_hiv", + "como_aids")) %>% +``` +Step 4. Filter on **usrds_ids** from the `patients` table +``` + filter(usrds_id %in% patients_filtered$usrds_id) +``` +Step 5. Keep first row of `medevid` data if a **usrds_id** has more than one, per the USRDS Researcher's Guide for de-duplicating the `medevid` table +``` +medevid_filtered = medevid_ids_filtered %>% + distinct(usrds_id, .keep_all = TRUE) %>% +``` +Step 6. Calculate the dialysis train time in days +``` + mutate( + masked_trnend = as_date(masked_trnend, origin = "1960-01-01"), + masked_trstdat = as_date(masked_trstdat, origin = "1960-01-01"), + + dial_train_time = as.double(difftime(masked_trnend, + masked_trstdat, + units = "days")) + ) +``` +***Points to consider*** + +1. It is important not to sort or alter the order of (or import into a SQL database) the `medevid` table before selecting the first entry per **usrds_id** for the `medevid` table. This order of the entries from the `medevid` table is curated as per the USRDS Researcher's Guide, which advises users to selected the first `medevid` entry for analysis. For other use cases, especially those requiring a longtitudinal dataset, the multiple MEDEVID records per patient may need to be retained. Decisions on how to handle the duplicated data should be made with the proposed use case in mind. + +2. Constructed features should be validated with clinicians to ensure that they are meaningful. For example, several preliminary features were initially constructed from the variables in the `medevid` table to serve as a proxy for prior nephrology care, such as whether a physician signature was present on the Medical Evidence form, the time between a patient signature and physician signature on the Medical Evidence Form, etc. After discussing these features with the clinicians on the team, it was determined that since a nephrologist's signature is required when a patient is referred for dialysis treatment, the signature date field is not a good proxy measure. + +## 6.2.6 Join Patients to Medevid + +***Steps to running the S1d_patients_medevid_join.R script*** + +This script creates the `patients_medevid` table in Postgres database. The `patients_medevid` table consists of the `patients` table with the data from the `medevid` table added via left join on **usrds_id**. + +**Input**: Postgres tables +``` +patients +medevid +``` +**Output**: Postgres table +``` +patients_medevid +``` +The result of this script produces the same 1,150,195 rows as we have in the `patients` table. + +Step 1. Left join the `patients` table and `medevid` table on **usrds_id** +``` +patients_medevid = left_join( + patients_filtered %>% select(-c( "cdtype")), + medevid_filtered %>% select(-c("randomoffsetindays", "disgrpc", "network", "inc_age", + "pdis", "sex", "race", "masked_died")), + by = "usrds_id" + ) +``` +The **cdtype** column is kept from the `medevid` table, the other duplicate colums are from the `patients` table + +Step 2. Populate any missing values for **sex** and **pdis** variables in `patients` with values from `medevid` (otherwise keep any duplicate columns from `patients`) +``` +patients_medevid = patients_medevid %>% + mutate( + sex = ifelse(is.na(sex), sex_med, sex), + pdis = ifelse(is.na(pdis), pdis_med, pdis) + ) %>% + select(-c(sex_med, pdis_med)) +``` + +## 6.2.7 Create Transplant Waitlist Features +The transplant data tables contains kidney transplant information from United Network for Organ Sharing (UNOS), such as information on transplant elibigility and transplant status. These features are created as transplant waitlist status and the amount of time on the transplant waitlist are indicative of a patients overall health status. Patients who are on the transplant waitlist are generally much healthier than those who are not listed. + +***Steps to running the S1e_patients_medevid_waitlist.R script*** + +This script creates the `waitseq_ki`, `waitseq_kp`, `tx`, `tx_waitlist_vars` and `patients_medevid_waitlist` tables in the Postgres database from the .csv files and the `patients_medevid` table. + +**Input**: csv files are produced in script [S1a-convertSAStoCSV.R](#623-convert-data-to-csv) +``` +patients_medevid +tx.csv +waitseq_kp.csv +waitseq_ki.csv +``` +**Output**: Postgres tables +``` +waitseq_ki +waitseq_kp +tx +tx_waitlist_vars +patients_medevid_waitlist +``` + +`patients_medevid_waitlist` is the full cohort that should be used from this point forward. + +The result of this script is the calculation of the following variables added to the `patients_medevid` table which will be saved as the `patients_medevid_waitlist` table. +``` +days_on_waitlist (number of days in transplant waitlist) +waitlist_status (active, transplanted, removed, never) +``` +Step 1. Import the `patients` table +``` +pat = dbGetQuery(con, + "SELECT * + FROM patients_medevid") +``` +Step 2. Import the **waitseq_ki.csv** file. +``` +waitseq_ki = read_csv(file.path(data_dir,"waitseq_ki.csv"), col_types = cols( + USRDS_ID = col_double(), + randomOffsetInDays = col_double(), + PROVUSRD = col_double(), + PID = col_double(), + masked_BEGIN = col_double(), + masked_ENDING = col_double() + )) +``` +Step 3. Transform column names to lowercase +``` +names(waitseq_ki) = tolower(names(waitseq_ki)) +``` +Step 4. Filter on rows with usrds_id in cohort. +``` +waitseq_ki = waitseq_ki %>% + filter(usrds_id %in% pat$usrds_id) %>% +``` +Step 5. Set **masked_begin** and **masked_ending** as dates. +``` + mutate(ws_list_dt = as_date(masked_begin, origin = "1960-01-01"), + ws_end_dt = as_date(masked_ending, origin = "1960-01-01"), + source = "ki") %>% +``` +Step 6. Keep only the following columns: +- ws_list_dt = New Waiting Period Starting Date +- ws_end_dt = New Waiting Period Ending Date +- provusrd = USRDS Assigned Facility ID +- source = 'ki' +- usrds_id +- pid +``` + select(usrds_id, pid, provusrd, ws_list_dt, ws_end_dt, source) +``` +Step 7. Save as the `waitseq_ki` table in Postgres +``` +fields = names(waitseq_ki) +drop_table_function(con, "waitseq_ki") +print(str_glue("create waitseq_ki in postgres")) +dbCreateTable( + con, + name = "waitseq_ki", + fields = waitseq_ki, + row.names = NULL +) +dbWriteTable( + con, + name = "waitseq_ki", + value = waitseq_ki, + row.names = FALSE, + append = TRUE +) +``` +Step 8. Import **waitseq_kp.csv** +``` +waitseq_kp = read_csv(file.path(data_dir,"waitseq_kp.csv"), col_types = cols( + USRDS_ID = col_double(), + randomOffsetInDays = col_double(), + PROVUSRD = col_double(), + PID = col_double(), + masked_BEGIN = col_double(), + masked_ENDING = col_double() +)) +names(waitseq_kp) = tolower(names(waitseq_kp)) +``` +Step 9. Filter on rows with usrds_id in cohort. +``` +waitseq_kp = waitseq_kp %>% + filter(usrds_id %in% pat$usrds_id) %>% +``` +Step 10. Set **masked_begin** and **masked_ending** as dates and save with new names **ws_list_dt** and **ws_end_dt**. +``` + mutate(ws_list_dt = as_date(masked_begin, origin = "1960-01-01"), + ws_end_dt = as_date(masked_ending, origin = "1960-01-01"), + source = "kp") %>% +``` +Step 11. Keep only the following columns: +- ws_list_dt = New Waiting Period Starting Date +- ws_end_dt = New Waiting Period Ending Date +- provusrd = USRDS Assigned Facility ID +- source = 'kp' +- usrds_id +- pid +``` + select(usrds_id, pid, provusrd, ws_list_dt, ws_end_dt, source) +``` +Step 12. Save as the `waitseq_kp` table in Postgres. +``` +fields = names(waitseq_kp) +drop_table_function(con, "waitseq_kp") +print(str_glue("create waitseq_kp in postgres")) +dbCreateTable( + con, + name = "waitseq_kp", + fields = waitseq_kp, + row.names = NULL +) +dbWriteTable( + con, + name = "waitseq_kp", + value = waitseq_kp, + row.names = FALSE, + append = TRUE +) +``` +Step 13. Concatenate waitseq_ki and waitseq_kp. +``` +waitseq = bind_rows(waitseq_ki, waitseq_kp) %>% + arrange(usrds_id, ws_list_dt) +``` +Step 14. Join the new waitseq table to `patients`. +``` +pat_waitseq = left_join( + pat %>% select(usrds_id, masked_first_se, masked_firstdial, + masked_can_first_listing_dt, masked_can_rem_dt, + masked_tx1date, masked_died, can_rem_cd, masked_tx1fail), + waitseq, + by = "usrds_id") %>% + arrange(usrds_id, ws_list_dt) +``` +Step 15. Label patients as ACTIVE on the waitlist and calculate the days on the transplant waitlist for ACTIVE patients + +Patients are labeled as "active" (those who are considered active on the transplant waitlist) if they meet one of the following criteria: +1. If list_date is before dial_date and end_date is on or after dial_date +2. Status is ACTIVE on the first day of dialysis + +First, check if earliest listing date from `waitseq` matches first listing date from `patients`. +``` +first_list = pat_waitseq %>% group_by(usrds_id) %>% + arrange(usrds_id, ws_list_dt) %>% + distinct(usrds_id, .keep_all = TRUE) %>% + ungroup(usrds_id) +``` +If **list_date** is before **dial_date** and **end_date** is on or after **dial_date**, OR if **list_dt** < **dial_dt** and **end_dt** == NA: +status is ACTIVE. +``` +pat_waitseq = pat_waitseq %>% + mutate(active = ifelse( + (ws_list_dt < masked_firstdial & ws_end_dt >= masked_firstdial) | (ws_list_dt < masked_firstdial & is.na(ws_end_dt)), 1, 0)) + +active = pat_waitseq %>% + filter((ws_list_dt < masked_firstdial & ws_end_dt >= masked_firstdial) | (ws_list_dt < masked_firstdial & is.na(ws_end_dt))) +``` +Calculate the days on the transplant waitlist for ACTIVE patients using **dial_dt** - **ws_list_dt**. +``` +active = active %>% + mutate( + days_on_waitlist = as.double(difftime(masked_firstdial, + ws_list_dt, + units = "days")) + ) +``` +Step 16. Remove ACTIVE patients from `pat_waitseq` + +Sort by **usrds_id** and **ws_list_dt** and keep the row with the earliest **ws_list_dt**. +``` +active = active %>% group_by(usrds_id) %>% + arrange(usrds_id, ws_list_dt) %>% + distinct(usrds_id, .keep_all = TRUE) %>% + ungroup(usrds_id) +``` +Remove active patients from `pat_waitseq`. Get unique **usrds_ids** in active dataframe. +``` +active_id = unique(active$usrds_id) +``` +Filter out rows from `pat_waitseq` where **usrds_id** is in the list of active USRDS IDs (**active_id**). +``` +pat_waitseq_not_act = pat_waitseq %>% + filter(!usrds_id %in% active_id) +``` +Step 17. Import the transplant dataset and process the data + +Import **tx.csv**. +``` +tx = read_csv(file.path(data_dir,"tx.csv"), col_types = cols( + DHISP = "c", + DSEX = "c", + RHISP = "c", + RSEX = "c" +)) + +names(tx) = tolower(names(tx)) +``` +Filter on rows with **usrds_id** in cohort. +``` +tx = tx %>% + filter(usrds_id %in% pat$usrds_id) %>% +``` +Transform **masked_tdate** to a date and save as **t_tx_dt**. +``` + mutate(t_tx_dt = as_date(masked_tdate, origin = "1960-01-01"), +``` +Transform **masked_faildate** to a date and save as **t_fail_dt**. +``` + t_fail_dt = as_date(masked_faildate, origin = "1960-01-01")) %>% +``` +Keep only the following columns: +- **t_tx_dt** = transplant date +- **t_fail_dt** = Transplant Failure Date +- **provusrd** = USRDS Assigned Facility ID +- **tottx** = Patient Total Number of Transplants +- **tx_srce** = Source of Transplant Record +- **usrds_id** +``` + select(usrds_id, provusrd, t_tx_dt, t_fail_dt, tottx, tx_srce) %>% + arrange(usrds_id, t_tx_dt) +``` +Step 18. Save as `tx` in Postgres database +``` +fields = names(tx) +drop_table_function(con, "tx") +print(str_glue("create tx in postgres")) +dbCreateTable( + con, + name = "tx", + fields = tx, + row.names = NULL +) +dbWriteTable( + con, + name = "tx", + value = tx, + row.names = FALSE, + append = TRUE +) +``` +Step 19. Construct TRANSPLANTED status + +Subset rows where LISTING DATE and LIST END DATE are both **BEFORE** DIAL START DATE +Subset rows with ws_list_dt (listing date) & ws_end_date (list end date) both **BEFORE** patient masked_firstdial (dialysis start date). +``` +list_before_dial = pat_waitseq_not_act %>% + filter(ws_list_dt < masked_firstdial & ws_end_dt < masked_firstdial) +``` +Group by **usrds_id**, sort by largest to smallest end_date, and keep the maximum **end_date** for each **usrds_id**. +``` +closest_end_dt_to_dial = list_before_dial %>% group_by(usrds_id) %>% + arrange(usrds_id, desc(ws_end_dt)) %>% + distinct(usrds_id, .keep_all = TRUE) %>% + ungroup(usrds_id) +``` +Left join `closest_end_dt_to_dial` and `tx` on **usrds_id**. This has effect of filtering `tx` dataset and keeping rows where **usrds_id** is in `closest_end_dt_to_dial`. + +If the maximum end date (**max_end_dt**) is equal to the transplant date (**t_tx_dt**), then the status is TRANSPLANTED. +``` +max_end_dt = left_join( + closest_end_dt_to_dial %>% select(-pid, -provusrd), + tx %>% select(usrds_id, t_tx_dt, t_fail_dt), + by = "usrds_id" +) + +max_end_dt = max_end_dt %>% + mutate(transplanted = if_else(is.na(t_tx_dt), 0, + if_else(ws_end_dt == t_tx_dt, 1, 0))) +transplanted = max_end_dt %>% + filter(ws_end_dt == t_tx_dt) +``` +Days on waitlist for TRANSPLANTED patients is **t_tx_dt** - **ws_list_dt**. +``` +transplanted = transplanted %>% + mutate( + days_on_waitlist = as.double(difftime(t_tx_dt, + ws_list_dt, + units = "days")) + ) +``` +Step 20. Construct REMOVED status + +Remove rows from **max_end_dt** where **usrds_id** is in `transplanted`. + +Get the unique **usrds_ids** in transplanted dataframe. +``` +transplanted_id = unique(transplanted$usrds_id) +``` +Filter out rows from **max_end_dt** where **usrds_id** is in **transplanted_id**. +``` +no_act_or_trans = max_end_dt %>% + filter(!usrds_id %in% transplanted_id) +``` +The remaining IDs should have REMOVED status. Check that all rows meet the removed criteria. +``` +num_no_act_tx = nrow(no_act_or_trans %>% + filter(ws_end_dt != t_tx_dt | is.na(t_tx_dt))) +``` +Create a REMOVED column and set **removed** = 1 if **ws_end_dt** != **t_tx_dt** or **t_tx_dt** = NA. +``` +no_act_or_trans = no_act_or_trans %>% + mutate(removed = if_else(ws_end_dt != t_tx_dt | is.na(t_tx_dt), 1, 0)) + +removed = no_act_or_trans %>% + filter(ws_end_dt != t_tx_dt | is.na(t_tx_dt)) +``` +Days on waitlist for REMOVED patients is **ws_end_dt** - **ws_list_dt**. +``` +removed = removed %>% + mutate( + days_on_waitlist = as.double(difftime(ws_end_dt, + ws_list_dt, + units = "days")) + ) +``` +Note: REMOVED only has duplicates because the `tx` table has duplicate rows for some patients, but the waitseq start and end dates are the same for both rows of each **usrds_id**, so only the first record is kept. +``` +removed = removed %>% group_by(usrds_id) %>% + arrange(usrds_id, ws_list_dt) %>% + distinct(usrds_id, .keep_all = TRUE) %>% + ungroup(usrds_id) +``` +Get unique **usrds_ids** in the `removed` dataframe. +``` +removed_id = unique(removed$usrds_id) +``` + +Step 21. Merge **days_on_waitlist** with **usrds_id** from active, transplanted, and removed. +``` +days = bind_rows(active %>% select(usrds_id, days_on_waitlist), + transplanted %>% select(usrds_id, days_on_waitlist), + removed %>% select(usrds_id, days_on_waitlist)) +days = days %>% arrange(usrds_id) +``` +Add ACTIVE patients to the `patients` table by setting all rows in `pat` where **usrds_id** is in **active_id** to ACTIVE = 1. +``` +pat = pat %>% + mutate(active = if_else(usrds_id %in% active_id, 1, 0)) %>% + select(usrds_id, active, masked_first_se, masked_firstdial, masked_can_first_listing_dt, + masked_can_rem_dt, masked_tx1date, masked_died, can_rem_cd, masked_tx1fail) +``` +Add TRANSPLANTED patients to the `patients` table by setting all rows in pat where **usrds_id** is in **transplanted_id** to TRANSPLANTED = 1. +``` +pat = pat %>% + mutate(transplanted = if_else(usrds_id %in% transplanted_id, 1, 0)) + +n_both = nrow(pat %>% filter(active == 1 & transplanted == 1)) +if (n_both!=0){ + print("WARNING! rows exist where active and transplanted are both == 1") +} +``` +Add REMOVED patients to the `patients` table by setting all rows in pat where **usrds_id** is in **removed_id** to REMOVED = 1. +``` +pat = pat %>% + mutate(removed = if_else(usrds_id %in% removed_id, 1, 0)) +``` +Step 22. Construct the NEVER status + +Set all rows where active, transplanted, and removed are all 0 to NEVER = 1. +``` +pat = pat %>% + mutate(never = if_else(active == 0 & transplanted == 0 & removed == 0, 1, 0)) +``` +Calculate the time on the waitlist. Join **days_on_waitlist** onto `patients` table. +``` +pat = left_join( + pat, + days, + by = "usrds_id" +) +``` +When **never** is 0, set **days_on_waitlist** to 0. +``` +pat = pat %>% + mutate(days_on_waitlist = replace_na(days_on_waitlist, 0)) +``` +Step 23. Reshape into long form with one **waitlist_status** variable. +``` +pat2 = pat %>% + mutate(waitlist_status = names( + pat %>% select( + active, transplanted, removed,never))[ + max.col(pat %>% select(active, transplanted, removed, never))]) +``` +Step 24. Save waitlist variables to Postgres. +``` +tx_waitlist_vars = pat2 %>% + select(usrds_id, waitlist_status, days_on_waitlist) %>% + arrange(usrds_id) + +csv_path = str_glue("{data_dir}tx_waitlist_vars.csv") +write_csv(tx_waitlist_vars,csv_path) +drop_table_function(con, "tx_waitlist_vars") +dbWriteTable( + con, + name = "tx_waitlist_vars", + value = tx_waitlist_vars, + row.names = FALSE, + append = TRUE) +``` +Step 25. Merge with `patients_medevid` and save to Postgres by adding the waitlist and transplant features to the `patient_medevid` table. +``` +patients_med = dbGetQuery(con, + "SELECT * + FROM patients_medevid") + +patients_med_waitlist = inner_join( + patients_med, + tx_waitlist_vars, + by="usrds_id" +) +fields = names(patients_med_waitlist) +print(str_glue("create patients_medevid_waitlist in postgres")) +drop_table_function(con, "patients_medevid_waitlist") +dbCreateTable( + con, + name = "patients_medevid_waitlist", + fields = patients_med_waitlist, + row.names = NULL +) +dbWriteTable( + con, + name = "patients_medevid_waitlist", + value = patients_med_waitlist, + row.names = FALSE, + append = TRUE +) +``` + +## 6.2.8 Create Partition Data + +***Steps for Running S2a_partitionData.R*** + +This script creates the `partition_10` table in Postgres which consists of **usrds_id** and **subset** and adds this **subset** column to the `patients_medevid_waitlist` table. +This **subset** column is the result of partitioning the number of rows (1,150,195) into 10 random subsets (numbered 0, 1, ..., 9) and assigning a patient identifier (**usrds_id**) to each subset. The purpose of partitioning the data is three-fold: +1. to ensure that there is no leakage between the training and test datasets (correctly stratify the classes) +2. to manage performance of imputation code (larger datasets require longer run times) +3. to ensure that the machine learning models are reproducible for any users (as opposed to setting the seed and using a library like caret to partition) + +Note: Each subset is *approximately* 10% because it is constructed completely at random. + +Input: `patients_medevid_waitlist` table from Postgres + +Output: `partition_10` table in Postgres + +Step 1. Define function to create **num_partitions** (10) indexed in a column named **subset** and save to Postgres as **partition_10** +``` +partition_data <- function(con, + usrds_id, + num_partitions, + data_tablename, + seed_value) { + + set.seed(2539) + + randvalue = runif( + length(usrds_id), + min = 0, + max = num_partitions + ) + + universe = cbind( + usrds_id, + floor(randvalue)) %>% + as.data.frame() + + names(universe) = c("usrds_id", "subset") + + tblname = str_glue("partition_{num_partitions}") + drop_table_function(con, tblname) + dbWriteTable( + con, + tblname, + universe, + append = FALSE, + row.names = FALSE + ) +} +``` +Step 2. Import the **usrds_ids** from Postgres. +``` +data_tbl = "patients_medevid_waitlist" + +usrds_id = dbGetQuery( + con, + str_glue( + " + SELECT usrds_id + FROM {data_tbl} + ORDER BY usrds_id + ")) +usrds_id = usrds_id$usrds_id +``` +Call the function defined above to create the 10 partitions. +``` +partition_data( + con, + usrds_id, + num_partitions = 10, + data_tablename = data_tbl + ) +``` + +## 6.2.9 Join `patients_medevid_waitlist` Table to the Partition Index + +***Steps to running the S2b_join_partition_data.R script*** + +This script joins the `patients_medevid_waitlist` table to the partition index. + +Input: `patients_medevid_waitlist` + +Output: `patients_medevid_waitlist` + +Step 1. Define a function to import and alter the `patients_medevid_waitlist` table by adding the **subset** column, and save to Postgres. +``` +join_data_partitions <- function(con, + data_tablename="patients_medevid_waitlist", + num_partitions=10){ + + dbSendStatement(con, str_glue( + " + ALTER TABLE {data_tablename} + ADD subset integer + "), n = -1) + + dbSendStatement( + con, + str_glue( + " + UPDATE {data_tablename} d + SET subset = p.subset + FROM partition_{num_partitions} p + WHERE d.usrds_id = p.usrds_id + "), n = -1) +} +``` +Step 2. Execute the function +``` +data_tbl = "patients_medevid_waitlist" + +join_data_partitions( + con, + data_tablename = data_tbl, + num_partitions = 10 +) +``` + +## 6.2.9.1 Calculate Demographic Subtotals Per Partition + +***Steps to running the S2c_calculate_partition_totals.R script*** + +This script creates a table with counts of select categories for each data partition to ensure that the partitions are representative of the entire dataset. + +Input: `patients_medevid_waitlist` + +Output: ./partition_totals_rev_method.csv + +Step 1. Pull the data from the database and count the number of patients per partition for the following variables: +- sex = male +- race = white +- number of missing hemoglobin values +- number of missing serum creatine values +- number of missing albumin values +- number of patients who died in the first 90 days (outcome variable) +``` +df = dbGetQuery( + con, + " + SELECT * + FROM patients_medevid_waitlist + " +) + +subsets_totals = df %>% + select(subset) %>% + group_by(subset) %>% + count() + +subsets_totals = rename(subsets_totals, c("total_pts"=n)) + +subsets_male = df %>% + filter(sex==1) %>% + select(sex, subset) %>% + group_by(sex, subset) %>% + count() +subsets_male <- rename(subsets_male, c("total_males"=n)) + +subsets_white = df %>% + filter(race==1) %>% + select(subset, race) %>% + group_by(subset,race) %>% + count() +subsets_white <- rename(subsets_white, c("total_white"=n)) + +subsets_heme = df %>% + filter(is.na(heglb)==TRUE) %>% + select(subset,heglb) %>% + group_by(heglb, subset) %>% + count() +subsets_heme <- rename(subsets_heme, c("total_heme_na"=n)) + +subsets_sercr = df %>% + filter(is.na(sercr)==TRUE) %>% + select(subset,sercr) %>% + group_by(sercr, subset) %>% + count() +subsets_sercr <- rename(subsets_sercr, c("total_sercr_na"=n)) + +subsets_album = df %>% + filter(is.na(album)==TRUE) %>% + select(subset,album) %>% + group_by(album, subset) %>% + count() +subsets_album <- rename(subsets_album, c("total_album_na"=n)) + +subsets_outcome = df %>% + filter(died_in_90==1) %>% + select(subset,died_in_90) %>% + group_by(died_in_90,subset) %>% + count() +subsets_outcome <- rename(subsets_outcome, c("total_died"=n)) + +dd = + left_join( + subsets_totals, + subsets_outcome, + by='subset' +) + +dd = left_join( + dd, + subsets_male, + by='subset' +) + +dd = left_join( + dd, + subsets_white, + by='subset' +) + +dd = left_join( dd, + subsets_heme, + by='subset' +) + +dd = left_join( dd, + subsets_album, + by='subset' +) + +dd = left_join( dd, + subsets_sercr, + by='subset' +) +write_csv(dd, "partition_totals_rev_method.csv") +``` +Table: Counts of select categories for each data partition +|Sub-set |Number of Males |Number of Race Group (White) |Number of Missing Hemoglobin Values |Number of Missing Serum Creatinine Values |Number of Missing Albumin Values |Total Number of Patients |Number of Patients who Died| +|--------|------|-------|-------|-------|-------|---------------|-----| +|0 |65,981 |76,535 |17,248 |2,055 |35,925 |114,824 |8,529| +|1 |66,131 |76,864 |17,108 |2,051 |35,129 |115,050 |8,773| +|2 |66,137 |76,773 |17,240 |2,043 |35,428 |115,044 |8,669| +|3 |66,031 |76,846 |17,406 |1,937 |35,100 |115,027 |8,426| +|4 |66,282 |76,788 |16,971 |1,917 |34,933 |114,802 |8,549| +|5 |66,042 |76,652 |17,285 |2,008 |35,138 |114,936 |8,671| +|6 |66,579 |77,002 |17,266 |1,976 |35,219 |115,207 |8,728| +|7 |66,332 |77,221 |17,266 |2,035 |35,019 |115,557 |8,695| +|8 |66,982 |76,605 |17,027 |2,014 |34,797 |114,925 |8,478| +|9 |66,033 |76,751 |16,847 |1,936 |34,973 |114,823 |8,565| + +## 6.2.10 Get Pre-ESRD Claims Data +The pre-ESRD claims tables in USRDS contains Medicare pre-ESRD Parts A and B, which are used to construct features for health care received prior to ESRD diagnosis. + +***Steps for running S3a_esrd_claims.R*** + +This script extracts, filters, and stores the pre-ESRD claims tables from 2011-2017 for the cohort. This script uses the [create_claim_table.R](#6211-create_claim_table-script) functions detailed in the next section. + +Input: csv files are produced in script [S1a-convertSAStoCSV.R](#623-convert-data-to-csv) +- [create_claim_table.R](#6211-create_claim_table-script) +- [pre_esrd_ip_claim_variables.R](#pre_esrd_ip_claim_variablesr) +- [pre_esrd_hs_claim_variables.R](#pre_esrd_hs_claim_variablesr) +- [pre_esrd_hh_claim_variables.R](#pre_esrd_hh_claim_variablesr) +- [pre_esrd_op_claim_variables.R](#pre_esrd_op_claim_variablesr) +- [pre_esrd_sn_claim_variables.R](#pre_esrd_sn_claim_variablesr) + +Output: The Postgres tables +``` +preesrd5y_ip_clm_inc +preesrd5y_hs_clm_inc +preesrd5y_hh_clm_inc +preesrd5y_op_clm_inc +preesrd5y_sn_clm_inc +``` +Step 1. Import the input file names and column types from the **pre_esrd_{xx}_claim_variables.R** scripts + +The types of claims include: +- Inpatient (IP) +- Outpatient (OP) +- Home health (HH) +- Hospice (HS) +- Skilled Nursing Unit (SN) + +``` +source('CreateDataSet/create_claim_table.R') + +claim_types = c( + "ip", + "hs", + "hh", + "op", + "sn" +) +``` +Step 2. Import and run the **create_claim_table** function for each claim type for years 2011-2017. +``` +for (typ in claim_types) { + source(str_glue("CreateDataSet/pre_esrd_{typ}_claim_variables.R")) + + create_claim_table( + data_dir, + con, + filenames_esrd, + fieldnames_esrd, + columns_esrd, + columns_esrd_2015, + table_name_pt='patients_medevid_waitlist' + ) +} +``` +***Points to consider*** + +Pre-ESRD claims data includes clinical as well as administrative information. Clinicians should be engaged to identify the variables in the claims data with predictive value. + +## 6.2.11 Create Claims Tables + +***Steps to running the create_claim_table.R script*** + +This script contains the functions used in **S3a_esrd_claims.R** to create the pre-ESRD claims tables. The schema for the tables changes from year to year. For example, there is no ***cdtype*** field prior to 2014, since all diagnosis codes were ICD9 prior to 2014. The script handles these year-to-year changes in schema. + +Step 1. Define **create_claim_table** function +``` +create_claim_table <- function( + data_dir, + con, + filenames, + fieldnames, + column_type, + column_type_2015, + table_name_pt) { + # send information to insert each year of claims data into the same Postgres table + + fieldnames = tolower(fieldnames) + for (filename in filenames) { + incident_year = + substr(filename, str_length(filename) - 3, str_length(filename)) + + if (incident_year < 2015) { + # claims prior to 2015 are all icd9, so we set cdtype to I for those years + csvfile = read_csv(file.path(data_dir, str_glue("{filename}.csv")), col_types = column_type_2015) + csvfile = csvfile %>% + mutate(cdtype = "I") + } + else { + csvfile = read_csv(file.path(data_dir, str_glue("{filename}.csv")), col_types = column_type) + } + + tblname = str_remove(filename, incident_year) + names(csvfile) = tolower(names(csvfile)) + fields = names(csvfile) + + patients = dbGetQuery( + con, + str_glue( + "SELECT usrds_id + FROM {table_name_pt}") + ) + + df = patients %>% + inner_join( + csvfile, + by = "usrds_id") %>% + mutate( + incident_year = incident_year) + + df$pdgns_cd = df$pdgns_cd %>% + trimws() %>% + str_pad(., + width = 7, + side = "right", + pad = "0") + + if (grepl('_ip_', tblname)){ + df = createIP_CLM(df, incident_year) + } + else { + df <- df %>% + filter(!is.na(masked_clm_from) & (masked_clm_from != "")) + } + # Append every set, except '2012' which will be the first table to import. + # this is b/c 2012 has the format that we want to use to create the table + # and append the other years since the format changes between 2011 and 2012-2017 + + if (incident_year==2012){ + drop_table_function(con, tblname) + print(str_glue("creating {tblname} claims using {incident_year}={nrow(df)} + patients={nrow(df %>% distinct(usrds_id, keep_all=FALSE))}")) + + dbWriteTable( + con, + tblname, + df[, fieldnames], + append = FALSE, + row.names = FALSE) + } + else { + print(str_glue("adding {incident_year} to {tblname}={nrow(df)} + patients={nrow(df %>% distinct(usrds_id, keep_all=FALSE))}")) + dbWriteTable( + con, + tblname, + df[, fieldnames], + append = TRUE, + row.names = FALSE) + } + } +} +``` +Step 2. Create a separate function **createIP_CLM** to handle the inpatient (IP) claims differently. This filters out rows with missing data. +``` +createIP_CLM = function(df, incident_year) { + # filtering for table named "preesrd5y_ip_clm" + print(str_glue("filtering IP claims {incident_year}")) + + df = df %>% + filter( + !is.na(masked_clm_from) & + (masked_clm_from != "") & + !is.na(drg_cd) & + (drg_cd != "") + ) + return(df) +} +``` + +## 6.2.12 Map Diagnosis Codes (drg_cd) to Primary Diagnosis Codes (pdgns_cd) +More information about the primary diagnosis codes and aggregate categories can be found in [Section 6.2.14 Diagnosis Groupings](#6212-diagnosis-groupings). + +***Steps to running the S3b_mapDrgCdToPdgnsCd.R script*** + +Prior to 2011, there is no **pdgns_cd** (primary diagnosis code) in the USRDS pre-ESRD data. This is an issue, because we need the **pdgns_cd** in order to map a claim to one of the 12 aggregate categories. This script addresses the issue by mapping the **drg_cd** (which *is* available prior to 2011) to a **pdgns_cd**. The mapping is not one-to-one. This script therefore constructs a probability distribution for the mapping, and the **pdgns_cd** is subsequently constructed based on this probability distribution. + +**Input:** Postgres table +``` +preesrd5y_ip_clm_inc +``` +**Output:** Postgres table +``` +drg_cd_mapping +``` + +The script `S3a-esrd_claims.R` must be run in order to generate the data used by this script. The in-patient claims have both **drg_cd** and **pdgns_cd**. These are used as the source data for mapping **drg_cd** to **pdgns_cd**. + +Step 1. Import data from the `preesrd5y_ip_clm_inc` table +``` +res = dbGetQuery( + con, + "WITH pre_drg_pdgn AS ( + SELECT drg_cd, pdgns_cd, COUNT(*) AS nmbr + FROM preesrd5y_ip_clm_inc + WHERE cdtype='I' + GROUP BY drg_cd, pdgns_cd), + drg_cd_tbl AS ( + SELECT drg_cd, pdgns_cd, nmbr, + row_number() OVER (PARTITION BY drg_cd + ORDER BY nmbr DESC) + FROM pre_drg_pdgn + ) + SELECT a.drg_cd, a.pdgns_cd, a.nmbr, a.row_number, SUM(b.nmbr) AS cum + FROM drg_cd_tbl a + INNER JOIN drg_cd_tbl b + ON a.drg_cd=b.drg_cd + AND a.row_number<=b.row_number + GROUP BY a.drg_cd, a.pdgns_cd, a.nmbr, a.row_number + ORDER BY a.drg_cd, a.row_number" +) +``` +Step 2. Aggregate table by **drg_cd** +``` +bydrgcd = res %>% + group_by(drg_cd) %>% + dplyr::summarise( + total = sum(as.numeric(nmbr))) +res = res %>% + inner_join( + bydrgcd, + by = "drg_cd") +res = res %>% + mutate( + cum0 = as.numeric(cum - nmbr), + cum = as.numeric(cum), + lb = cum0 / total, + ub = cum / total +) +``` +Step 3. Select the columns to save. +``` +drg_cd_mapping = res %>% + select( + drg_cd, + pdgns_cd, + lb, + ub) +``` +Step 4. Save to Postgres as `drg_cd_mapping` +``` +drg_tblname = "drg_cd_mapping" +drop_table_function(con, drg_tblname) +dbWriteTable(con, + drg_tblname, + drg_cd_mapping, + append = F, + row.names = FALSE) +``` + +## 6.2.13 Get pre-2011 pre-ESRD Claims Data + +***Steps to running the S3c_esrd_claims_pre_2011.R script*** + +Before 2011, pre-ESRD claims are stored in the files inc2008.csv, inc2009.csv, inc2010.csv. The files are organized differently from the other pre-ESRD files: the type of claim is not part of the file name (instead, it is identified in the file's contents in a field called "hcfasaf"); and the contents of the file can differ from year to year. Also, the **pdgns_cd** is not available prior to 2012. This script constructs a **pdgns_cd** from the **drg_cd** which is available prior to 2011. + +**Input:** .csv files are produced in script [S1a-convertSAStoCSV.R](#623-convert-data-to-csv) +- [pre_esrd_pre2011_claim_variables.R](#pre_esrd_pre2011_claim_variablesr) +``` +inc2008.csv +inc2009.csv +inc2010.csv +drg_cd_mapping +``` +**Output**: Rows of pre-2011 claims for the cohort added to the following Postgres tables +``` +preesrd5y_ip_clm_inc +preesrd5y_hh_clm_inc +preesrd5y_hs_clm_inc +preesrd5y_op_clm_inc +preesrd5y_sn_clm_inc +``` +File names and column types are defined in [pre_esrd_pre2011_claim_variables.R](#pre_esrd_pre2011_claim_variablesr) +``` +source('CreateDataSet/pre_esrd_pre2011_claim_variables.R') +``` +Step 1. Import the pre-2011 claims and filter on **usrds_ids** in the cohort and features in the post-2011 claims. +- Set cdtype = "I" to indicate ICD-9 +- Set any missing drg_cd=000. +``` +create_pre_2011 <- function( + data_dir, + filename, + tblname, + append_flag, + table_name_pt, + newIn2010, + column_types){ + + inc20xx = read_csv(file.path(data_dir, str_glue("{filename}.csv")), col_types=column_types) + incident_year = + substr(filename, str_length(filename) - 3, str_length(filename)) + names(inc20xx) = tolower(names(inc20xx)) + + patients = dbGetQuery( + con, + str_glue( + "SELECT usrds_id + FROM {table_name_pt}") + ) + + # filter on ids from the patient table + inc20xx = inc20xx %>% + filter( + usrds_id %in% patients$usrds_id) %>% + mutate( + incident_year = incident_year, + cdtype = "I", + drg_cd = ifelse( + is.na(drg_cd), "000", drg_cd), + drg_cd = ifelse( + drg_cd == "", "000", drg_cd)) %>% + mutate( + drg_cd = as.numeric(drg_cd)) + + sortednm = names(inc20xx) %>% sort() + inc20xx = inc20xx[, sortednm] + + if (append_flag==FALSE){ + inc20xx[, newIn2010] = NA + drop_table_function(con, tblname) + } + print(nrow(inc20xx)) + dbWriteTable( + con, + tblname, + inc20xx, + append = append_flag, + row.names = FALSE) +} +``` +Step 2: For each claim type (home health - hh, hospice - hs, inpatient - ip, skilled nursing unit - sn, outpatient - op) +- Generate a uniform random number for each record in pre2011 claims, + and look up **pdgns_cd** from **drg_cd_mapping** based on this random number, + which will produce a **pdgns_cd** reflecting the underlying + joint distribution of (**drg_cd**, **pdgns_cd**) in the data +``` +get_claim_type_x <- function(claim_type, table_nm) { + print(str_glue("get {claim_type}")) + df = dbGetQuery( + con, + str_glue( + " + SELECT * + FROM {table_nm} + WHERE hcfasaf='{claim_type}' + ")) + return(df) +} +get_distribution <- function(df){ + # Generate a uniform rv for each record in df, and look up pdgns_cd from drg_cd_mapping + # based on this rv, which will produce a pdgns_cd reflecting the underlying + # joint distribution of (drg_cd,pdgns_cd) in the data + + print("get distribution of drg_cd, pdgns_cd") + set.seed(597) + + df$rv = runif( + dim(df)[1] + ) + temptablename = "temp_df" + + drop_table_function(con, temptablename) + + dbWriteTable( + con, + temptablename, + df, + temporary = TRUE + ) + dg = dbGetQuery( + con, + str_glue( + " + SELECT a.*, b.pdgns_cd + FROM {temptablename} a + LEFT JOIN drg_cd_mapping b + ON a.drg_cd = b.drg_cd + AND a.rv <= b.ub + AND a.rv > b.lb + ")) + return(dg) +} +``` +Step 3: Insert these rows into the main Postgres table for this claim type. +``` +insert_claim_rows <- function(claim_type, pre2011_data) { + #Get the field names to be inserted into the pre-esrd data, + # in the correct order + print(str_glue("intert pre 2011 {claim_type} rows into table {nrow(pre2011_data)}")) + main_fieldnames = names( + dbGetQuery( + con, + str_glue( + " + SELECT * + FROM preesrd5y_{claim_type}_clm_inc + LIMIT 10 + ") + ) + ) + + #Set fields in main claims fieldnames that do not appear in the pre2011 data = nan + pre2011_data[, setdiff(main_fieldnames, names(pre2011_data))] = NA + + # Include only fields also in main_fieldnames, in the proper order + pre2011_data = pre2011_data[, main_fieldnames] + + # append pre2011 rows to the main claims table + main_tblname = str_glue("preesrd5y_{claim_type}_clm_inc") + dbWriteTable( + con, + main_tblname, + pre2011_data, + append = TRUE, + row.names = FALSE) + } +``` +Step 4. Define the wrapper function to separate into each year and claim type and save to Postgres tables. +``` +source_pre_2011 <- function(data_dir, tblname, column_types) { + + newIn2010 = c( + "dpoadmin", + "dpodose", + "hgb", + "dpocash", + "attending_phys", + "operating_phys", + "other_phys" + ) + + create_pre_2011(data_dir, + "inc2010", + tblname, + append_flag=FALSE, + table_name_pt = "patients_medevid_waitlist", + newIn2010, + column_types) + + create_pre_2011(data_dir, + "inc2009", + tblname, + append_flag=TRUE, + table_name_pt = "patients_medevid_waitlist", + newIn2010, + column_types) + + create_pre_2011(data_dir, + "inc2008", + tblname, + append_flag=TRUE, + table_name_pt = "patients_medevid_waitlist", + newIn2010, + column_types) + + ########BEGIN HOME HEALTH####### + df = get_claim_type_x("H",tblname) + dg = get_distribution(df) + insert_claim_rows("hh", dg) + rm(df,dg) + + ####BEGIN HOSPICE########## + df = get_claim_type_x("S", tblname) + dg = get_distribution(df) + insert_claim_rows("hs", dg) + rm(df,dg) + + ####BEGIN INPATIENT####### + df = get_claim_type_x("I", tblname) + dg = get_distribution(df) + insert_claim_rows("ip", dg) + rm(df,dg) + + ###BEGIN SKILLED NURSING#### + df = get_claim_type_x("N", tblname) + dg = get_distribution(df) + insert_claim_rows("sn", dg) + rm(df,dg) + + ####BEGIN OUTPATIENT#### + df = get_claim_type_x("O", tblname) + + # Step 2: Generate a uniform rv for each record in df, and look up pdgns_cd from drg_cd_mapping + # based on this rv, which will produce a pdgns_cd reflecting the underlying + # joint distribution of (drg_cd, pdgns_cd) in the data + set.seed(597) + df$rv = runif( + dim(df)[1] + ) + temptablename = "temp_df" + drop_table_function(con, temptablename) + dbWriteTable( + con, + temptablename, + df, + temporary = TRUE + ) + + make_query <- function(dg_vals, temptablename){ + dg = str_glue( + "WITH w as ( + SELECT * + FROM {temptablename} + WHERE MOD(CAST(usrds_id AS NUMERIC),10) IN ({dg_vals}) + ) + SELECT a.*, b.pdgns_cd + FROM w a + LEFT JOIN drg_cd_mapping b + ON a.drg_cd = b.drg_cd + AND a.rv <= b.ub + AND a.rv > b.lb" + ) + return(dg) + } + dg_1 = dbGetQuery(con, make_query("0,1", temptablename)) + + dg_2 = dbGetQuery(con, make_query("2,3", temptablename)) + + dg_3 = dbGetQuery(con, make_query("4,5", temptablename)) + + dg_4 = dbGetQuery(con, make_query("6,7", temptablename)) + + dg_5 = dbGetQuery(con, make_query("8,9", temptablename)) + + dg = rbind(dg_1, dg_2) + dg = dg %>% + rbind(dg_3) %>% + rbind(dg_4) %>% + rbind(dg_5) + + #step 3 append rows to main table + insert_claim_rows("op", dg) +} +``` +Step 5. Execute all functions defined above +``` +source_pre_2011(data_dir,"pre_esrd_2011", columns_esrd_2015) +``` + +## 6.2.14 Diagnosis Groupings +There are several thousand primary diagnosis codes in pre-ESRD claims data, which need to be meaningfully categorized in order to create useful features. 12 major disease groups that were determined by the clinicians on the project include: diabetes, hypertension, heart failure, cardiovascular arterial disease, cerebrovascular disease, peripheral arterial disease, pneumonia, kidney failure, malignant neoplasm, smoking, alcohol dependence, and drug dependence. + +***Steps for running S3d_dxCodeGrouping.R*** + +This script maps each valule in pdgns_cd column in the pre-ESRD data to one of 12 aggregated diagnosis groupings, and stores the mapping in the `dxmap` Postgres table. Two sources of input are used for the groupings: CCS (Clinical Classification System) and UCSF physician expertise. + +**Input**: +- [icd9_ccs_codes.R](#icd9_ccs_codesr) (for CCS groupings) +- [icd10_ccs_codes.R](#icd10_ccs_codesr) (for CCS groupings) +- [icd9_dx_2014.txt](#icd9_dx_2014txt) (for the icd9 pdgsn_cd) +- [icd10_dx_codes.txt](#icd10_dx_codestxt) (for the icd10 pdgsn_cd) +- [dx_mappings_ucsf.csv](#dx_mappings_ucsfcsv) (for UCSF-advised categorizations of diagnosis codes) + +**Output**: +``` +dxmap +``` + +Step 1. Define functions +``` +read_icd9 <- function(directory, filename) { + #READ IN ICD9 SOURCE DATA + lines = readLines(file.path(directory,filename)) + lines = + iconv(lines[2:length(lines)], + from = "latin1", + to = "ASCII", + sub = "" + ) + + #Convert utf-8 to ASCII and remove special characters like umlauts and accents + pdgns_cd = substr(lines, 1, 6) %>% + trimws() %>% + str_pad(., + width = 7, + side = "right", + pad = "0" + ) + description = substr(lines, 7, 130) + + df9 = as.data.frame(cbind(pdgns_cd, description)) + df9$cdtype = "I" + return(df9) +} +read_icd10 <- function(directory, filename){ + lines = readLines(file.path(directory, filename)) + lines <- + iconv(lines[2:length(lines)], + from = "latin1", + to = "ASCII", + sub = "" + ) + pdgns_cd = substr(lines, 1, 7) %>% + trimws() %>% + str_pad(., + width = 7, + side = "right", + pad = "0" + ) + description = substr(lines, 11, 130) + df10 = as.data.frame(cbind(pdgns_cd, description), stringsAsFactors = F) + df10 = df10 %>% filter(pdgns_cd != '0000000') + #There may be multiple entries with the same pdgns_cd for icd10, so choose one + df10 = sqldf( + " + SELECT pdgns_cd, MAX(description) AS description + FROM df10 + GROUP BY pdgns_cd" + ) + df10$cdtype = "D" + return(df10) +} +map_pdgns = function(df9, df10){ + # join icd9 and icd10 + df <- as.data.frame(rbind(df9, df10)) %>% + mutate_at( + vars('cdtype', 'pdgns_cd', 'description'), + as.character + ) + df = df %>% + mutate( + dx_neo = as.integer( + grepl("malignant neoplasm", tolower(df$description)) & + grepl("family history", tolower(df$description)) + ), + # dx_poi=as.integer(grepl("poisoning",tolower(df$description))), + dx_smo = as.integer(( + cdtype == 'D' & pdgns_cd %in% smo_10 + ) |( + cdtype == 'I' & pdgns_cd %in% smo_9) + ), + dx_alc = as.integer(( + cdtype == 'D' & pdgns_cd %in% alc_10 + ) | ( + cdtype == 'I' & pdgns_cd %in% alc_9) + ), + dx_drg = as.integer(( + cdtype == 'D' & pdgns_cd %in% drg_10 + ) | ( + cdtype == 'I' & pdgns_cd %in% drg_9) + ), + dx_pne = as.integer(( + cdtype == 'D' & pdgns_cd %in% pne_10 + ) | ( + cdtype == 'I' & pdgns_cd %in% pne_9) + ), + dx_kid = as.integer(( + cdtype == 'D' & pdgns_cd %in% kid_10 + ) | ( + cdtype == 'I' & pdgns_cd %in% kid_9) + ) + ) + return(df) +} +getComorbids <- function(directory, filename, df, colname, prefix = 'dx_') { + ucsf_mappings = read.csv(file.path(directory, filename), stringsAsFactors = FALSE) + dg = sqldf( + "SELECT df.*, b.label + FROM df + LEFT JOIN ucsf_mappings b + ON df.pdgns_cd>=b.lb + AND df.pdgns_cd<=b.ub", + method = "raw" + ) + values = unique(dg[, colname]) %>% setdiff(NA) + for (v in values) { + dg[, paste0(prefix, v)] = (as.integer(dg[, colname] == v)) + dg[, paste0(prefix, v)] = replace_na(dg[, paste0(prefix, v)], 0) + } + dg$label = NULL + return(dg) +} +``` +Step 2. Execute Functions +``` +df9 = read_icd9(source_dir, "icd9_dx_2014.txt") +df10 = read_icd9(source_dir, "icd10_dx_codes.txt") +mapped9_10 = map_pdgns(df9, df10) +dh = getComorbids(source_dir, "dx_mappings_ucsf.csv", df=mapped9_10, colname = "label") +``` +Step 3. Save to Postgres database as `dxmap` +``` +drop_table_function(con, "dxmap") +tblname = "dxmap" +dbWriteTable( + con, + tblname, + dh, + append = FALSE, + row.names = FALSE + ) +``` + +***Points to consider*** + +The primary diagnosis codes in the pre-ESRD claims should be converted with clinician’s input into relevant disease groupings that can be used to create features with predictive value. It is difficult find a one-size-fits-all method for mapping diagnosis codes to meaningful categories as the categories are highly dependent on the use case. Future researchers may want to consider alternative disease groupings that are informed by clinicians and other health-care researchers. + +## 6.2.15 Aggregate Pre-ESRD Claims Data + +***Steps for running S4a_pre_esrd_full.R*** + +USRDS data have multiple pre-ESRD claims per patient. This script aggregates the data for each patient through the following steps: +1) Merge the pre-ESRD claims tables +2) Construct counts of claims grouped by type of claim and diagnosis code +3) Create one record per patient, with all pre-ESRD summary statistics aggregated for each patient +4) Create binary variables to indicate the presence or absence of pre-ESRD claims and of each type of claim (IP, HH, HS, OP, SN) + +The record includes total number of claims and total length of stay, grouped by: +1. Type of claim (IP, HH, HS, OP, SN) and +2. The aggregated diagnosis grouping. + +**Input**: +- [setfieldtypes.R](#setfieldtypesr) +``` +preesrd5y_ip_clm_inc +preesrd5y_hs_clm_inc +preesrd5y_hh_clm_inc +preesrd5y_op_clm_inc +preesrd5y_sn_clm_inc +patients_medevid_waitlist +``` +**Output**: +``` +preesrdfeatures +``` + +Table: Number of unique patients with each type of Medicare Pre-ESRD claims +| |Inpatient (IP) |Outpatient (OP) |Skilled Nursing Unit (SN) |Home Health (HH) |Hospice (HS)| +|--------|-------|-----------------------|-------------------------------|-----------------------|------------| +|Number of Unique Patients |553,704 |514,926 |140,417 |224,272 |12,482| +|Total Number of Claims |2,496,683 |15,222,280 |592,970 |939,751 |50,200| + +Step 1. Define functions for SQL queries to get claim information for 3 types of aggregations and join to the `dxmap` table. +``` +prepareQuery = function(dxcols, tablename, qryAggType = 1, testMode = 0) { + + qry_pt1=paste0("b.", dxcols$column_name, collapse=",") + + if (qryAggType == 1) { + vec1 = paste0("SUM(stay*", dxcols$column_name, ")") + vec2 = paste0(" AS stay", substr(dxcols$column_name, 3, 6)) + qry_pt5 = paste0(vec1, vec2, collapse = ", ") + + } else if (qryAggType == 2) { + vec1 = paste0("SUM(", dxcols$column_name, ")") + vec2 = paste0(" AS clms", substr(dxcols$column_name, 3, 6)) + qry_pt5 = paste0(vec1, vec2, collapse = ", ") + + } else if (qryAggType == 3) { + vec1 = paste0("MAX(", dxcols$column_name, ")") + vec2 = paste0(" AS has", substr(dxcols$column_name, 3, 6)) + qry_pt5 = paste0(vec1, vec2, collapse = ", ") + } + + qry_main = str_glue("WITH w AS ( + SELECT a.usrds_id, + a.pdgns_cd, + a.masked_clm_thru-a.masked_clm_from AS stay, + a.cdtype, + a.hgb, + a.hcrit, + {qry_pt1} + FROM {tablename} a + LEFT JOIN dxmap b + ON a.cdtype=b.cdtype + AND a.pdgns_cd=b.pdgns_cd + ) + SELECT usrds_id, {qry_pt5} + FROM w + GROUP BY usrds_id" + ) + return(qry_main) +} +``` +Step 2. Get column names +``` +dxcols = names(dbGetQuery( + con, + " + SELECT * + FROM dxmap + LIMIT 5 + ")) + +dxcols = dxcols[4:length(dxcols)] %>% as.data.frame() +names(dxcols) = "column_name" +``` +Step 3. Send a SQL query for each type of claim and aggregation. +``` +ip1 = dbGetQuery(con,prepareQuery( + dxcols, + "preesrd5y_ip_clm_inc", + qryAggType = 1, + testMode = 0 + )) + +ip2 = dbGetQuery(con,prepareQuery( + dxcols, + "preesrd5y_ip_clm_inc", + qryAggType = 2, + testMode = 0 + )) + +ip3 = dbGetQuery(con,prepareQuery( + dxcols, + "preesrd5y_ip_clm_inc", + qryAggType = 3, + testMode = 0 + )) + +op1 = dbGetQuery(con,prepareQuery( + dxcols, + "preesrd5y_op_clm_inc", + qryAggType = 1, + testMode = 0 + )) + +op2 = dbGetQuery(con,prepareQuery( + dxcols, + "preesrd5y_op_clm_inc", + qryAggType = 2, + testMode = 0 + )) + +op3 = dbGetQuery(con,prepareQuery( + dxcols, + "preesrd5y_op_clm_inc", + qryAggType = 3, + testMode = 0 + )) + +sn1 = dbGetQuery(con, prepareQuery( + dxcols, + "preesrd5y_sn_clm_inc", + qryAggType = 1, + testMode = 0 + )) + +sn2 = dbGetQuery(con, prepareQuery( + dxcols, + "preesrd5y_sn_clm_inc", + qryAggType = 2, + testMode = 0 + )) + +sn3 = dbGetQuery(con, prepareQuery( + dxcols, + "preesrd5y_sn_clm_inc", + qryAggType = 3, + testMode = 0 + )) +``` +Step 4. Calculate MAX(masked_clm_thru)-MIN(masked_clm_from) as the time range of claims for each patient. +``` +prepareAggQuery = function(clm_type) { + qry_main = str_glue("SELECT usrds_id, + SUM(masked_clm_thru-masked_clm_from) AS stay, + MAX(masked_clm_thru)-MIN(masked_clm_from) AS range, + MIN(masked_clm_from) AS earliest_clm, + MAX(masked_clm_thru) AS latest_clm, + COUNT(*) AS claims + FROM preesrd5y_{clm_type}_clm_inc + GROUP BY usrds_id" + ) + return(qry_main) +} + +hha = dbGetQuery(con, prepareAggQuery("hh")) +ipa = dbGetQuery(con, prepareAggQuery("ip")) +opa = dbGetQuery(con, prepareAggQuery("op")) +sna = dbGetQuery(con, prepareAggQuery("sn")) +hsa = dbGetQuery(con, prepareAggQuery("hs")) +``` +Note: A large amount of code devoted to creating queries is not included in this guide. See the code for details. + +Step 5. Get claims_range +``` +df = dbGetQuery(con, qry) + +earliest_cols = names(df)[grepl("earliest_clm", names(df))] +latest_cols = names(df)[grepl("latest_clm", names(df))] +for (c in earliest_cols) { + df[, c] = ifelse(is.na(df[, c]), 500000, df[, c]) +} +for (c in latest_cols) { + df[, c] = ifelse(is.na(df[, c]), -500000, df[, c]) +} + +earliest_claim_date = apply(df[, earliest_cols], 1, "min") +latest_claim_date = apply(df[, latest_cols], 1, "max") +df$claims_range = latest_claim_date - earliest_claim_date + +cols_to_delete = union(earliest_cols, latest_cols) +df[, cols_to_delete] = NULL +``` +Out of the individual columns named "has_dx_claimtype" (e.g., "has_neo_ip") create a single column "has_dx" +``` +has_cols = names(df)[grepl("has_", names(df))] +``` +Step 6. Create a list of diagnosis groupings +``` +dxs = unique( + substr( + has_cols, 5, 7)) +``` +Step 7. Create a binary result to yield 1 if the patients has any present, 0 if not, na if all are nans +- Example 1: has_dia_ip=NA, has_dia_op=0, has_dia_sn=1. So x=c(NA,0,1). Then returns 1 +- Example 2: x=c(NA,NA,NA). Then returns NA +- Example 3: x=c(NA,0,NA). Then returns 0 +``` +mymax = function(x) { + p_sum = sum(x > 0, na.rm = T) #number of positive elements + z_sum = sum(x == 0, na.rm = T) #number of zero elements + return(ifelse(p_sum > 0, 1, ifelse(z_sum > 0, 0, NA))) +} +``` +Use this so we end up with NA if a vector is all NA +``` +safe.max = function(invector) { + na.pct = sum(is.na(invector))/length(invector) + if (na.pct == 1) { + return(NA) } + else { + return(max(invector,na.rm=TRUE)) + } +} +``` +For each diagnosis grouping +``` +for (c in dxs) { + hasdxcols = has_cols[grepl(c, has_cols)] + df[,paste0("has_",c)]=apply( + df[,hasdxcols], + 1, + function(x) safe.max(as.numeric(x)) + ) +} + +hasvars = names(df)[grepl("has_", names(df))] +hasvarsettings = hasvars[grepl("_ip$|_op$|_sn$|_hh$|_hs$", hasvars)] +df[, hasvarsettings] = NULL #remove variables like "has_neo_ip", keeping in "has_neo" +df$claims_range = ifelse(df$claims_range < 0, NA, df$claims_range) +``` +Step 8. Create a binary feature for each claim type. These are used in the parametric models instead of the detailed claim numbers. +``` +df$prior_hh_care = as.integer(df$claims_hh > 0 & + !is.na(df$claims_hh)) +df$prior_hs_care = as.integer(df$claims_hs > 0 & + !is.na(df$claims_hs)) +df$prior_ip_care = as.integer(df$claims_ip > 0 & + !is.na(df$claims_ip)) +df$prior_op_care = as.integer(df$claims_op > 0 & + !is.na(df$claims_op)) +df$prior_sn_care = as.integer(df$claims_sn > 0 & + !is.na(df$claims_sn)) +priorvars = names(df)[grepl("prior_", names(df))] + +df$has_preesrd_claim = apply( + df[, priorvars], + 1, + function(x) safe.max(as.numeric(x)) +) +``` +Step 9. Save to Postgres as `preesrdfeatures` +``` +drop_table_function(con, pre_esrd_tblname) +dbWriteTable( + con, + pre_esrd_tblname, + df, + field.types = myfieldtypes, + append = FALSE, + row.names = FALSE +) +``` + +## 6.2.16 Join the `preesrdfeatures` Tables to the Partition Index + +***Steps to running the S4b_join_to_partition_data.R script*** + +Join the `preesrdfeatures` tables to our partition index. + +**Input**: +``` +preesrdfeatures +partition_10 +``` +**Output**: +``` +preesrdfeatures +``` + +Step 1. Define a function to import and alter the `preesrdfeatures` table by adding the **subset** column, and save to Postgres. +``` +join_data_partitions <- function(con, + data_tablename="preesrdfeatures", + num_partitions=10){ + + dbSendStatement(con, str_glue( + " + ALTER TABLE {data_tablename} + ADD subset integer + "), n = -1) + + dbSendStatement( + con, + str_glue( + " + UPDATE {data_tablename} d + SET subset = p.subset + FROM partition_{num_partitions} p + WHERE d.usrds_id = p.usrds_id + "), n = -1) +} +``` +Step 2. Execute the function +``` +data_tbl = "preesrdfeatures" + +join_data_partitions( + con, + data_tablename = data_tbl, + num_partitions = 10 +) +``` + +## 6.2.17 Map ICD-9 to ICD-10 + +***Steps for S5_pdis_mapping.R*** + +This script maps ICD-9 to ICD-10 codes and creates a table named `pdis_recode_map` which is used in *S6-Prepare Data Set*, for assigning **pdis** to a numeric value called **pdis_recode**. The [2017_I9gem_map.txt](#2017_I9gem_maptxt) is used for this purpose. + +PDIS (primary disease causing renal failure) is either the ICD-9 or ICD-10 code for the primary cause of renal failure depending on the year of the claim. Claims prior to 2015 contain ICD-9 codes whereas claims post-2016 contain ICD-10 codes. Claims from 2015 and 2016 can be either ICD-9 or ICD-10. The format for the **pdis** variable in the USRDS data is as a character variable. The ICD-9 codes were mapped to their ICD-10 equivalents to preserve the original meaning of the character variable in the numeric encoding. It was re-coded by +- Mapping all codes to their ICD-10 equivalent +- Converting them to a factor +- Typing them to a numeric + +**Input**: The **pdis** column from `patients_medevid_waitlist` (originally comes from the `patients` table) +``` +2017_I9gem_map.txt +patients_medevid_waitlist +``` +**Output**: +``` +pdis_recode_map +``` + +Step 1. Import the data from Postgres. +``` +df1 = dbGetQuery(con, + "SELECT * + FROM patients_medevid_waitlist") +``` +Whether the code is ICD-9 or ICD-10 is determined by the column **cdtype**. In order to create a map, we must get each unique **pdis** value for each **cdtype** where **cdtype** is NOT missing (NULL). Exclude entries where **cdtype** is missing (NULL). (There are 20,003 patients where **cdtype** is missing.) +``` +pdis_occurrences = dbGetQuery(con, + "SELECT cdtype, pdis, COUNT(*) AS nmbr + FROM patients_medevid_waitlist + WHERE cdtype IS NOT NULL + GROUP BY pdis, cdtype" +) +``` +Step 2. Standardize the format so that we can match with another pdis file. +``` +pdis_occurrences$pdis = pdis_occurrences$pdis %>% + trimws() %>% + str_pad(., + width = 7, + side = "right", + pad = "0" + ) +``` +Step 3. Import [2017_I9gem_map.txt](#2017_I9gem_maptxt) +``` +map_icd_9_to_10 = read.table(file = file.path(source_dir, "2017_I9gem_map.txt"), + header = TRUE) %>% + select(icd9, icd10) +``` +Step 4. Format columns +``` +map_icd_9_to_10 = map_icd_9_to_10 %>% + mutate(icd9 = icd9 %>% + trimws() %>% + str_pad(., + width = 7, + side = "right", + pad = "0" + ), + icd10 = icd10 %>% + trimws() %>% + str_pad(., + width = 7, + side = "right", + pad = "0" + ) +) +``` +ICD-10: The character-level **pdis_recode** is same as **pdis** when **cdtype** equals "D" (indicating ICD-10) +``` +pdis_occurrences_D = pdis_occurrences %>% + filter(cdtype == "D") %>% + mutate(pdis_recode_char = pdis) +``` +Step 5. Use the crosswalk to map the ICD-9 codes to ICD-10 +``` +pdis_occurrences_I = sqldf( + "SELECT a.*, b.icd10 AS pdis_recode_char + FROM pdis_occurrences a + LEFT JOIN map_icd_9_to_10 b + ON a.pdis=b.icd9 + WHERE a.cdtype='I'", + method = "raw" +) +``` +Step 6. Concatenate the 2 maps +``` +pdis_recode_map = union(pdis_occurrences_D, pdis_occurrences_I) +pdis_recode_map = pdis_recode_map %>% + mutate(pdis_recode = as.factor(pdis_recode_char) %>% as.numeric()) +``` +Step 7. Calculate the sum of each recode value when recode is not missing (NaN) +``` +pdis_recode_agg = pdis_recode_map %>% + group_by(pdis_recode) %>% + dplyr::summarise(pdis_recode_nmbr = sum(nmbr)) %>% + as.data.frame() + +pdis_recode_map = pdis_recode_map %>% left_join(pdis_recode_agg, by = "pdis_recode") +``` +Step 8. Save to Postgres as `pdis_recode_map` +``` +tblname = "pdis_recode_map" +drop_table_function(con, tblname) +dbWriteTable(con, + tblname, + pdis_recode_map, + append = FALSE, + row.names = FALSE) +``` + +## 6.2.18 Prepare Data for Modeling + +***Steps for running S6-prepareDataSet.R script*** + +This script creates `medxpreesrd` and uses the full dataset `patients_medevid_waitlist` and `preesrdfeatures` to construct the table `medxpreesrd` by: +- creating binary variables to indicate whether imputed values are missing or out of bounds for a given patient +- encoding character values to numeric +- counting the number of value types for como_* columns +- incorporating **pdis_recode** column +- deleting features not used for modeling + +**Input**: +``` +patients_medevid_waitlist +preesrdfeatures +pdis_recode_map +dxmap +imputation_rules.xlsx +``` +**Output**: +``` +medxpreesrd +``` +Step 1. Define variables +``` +subsets = "0, 1" +tablename = "patients_medevid_waitlist" +table_preesrd = "preesrdfeatures" +medex_tblname = "medxpreesrd" +``` +Step 2. Import 2 partitions of data (subsets = "0, 1") from `patients_medevid_waitlist`. This code should be run 5 times (for the 10 partitions/subsets), but this example will be for 1 loop. The code contains the details to run the functions for the remaining partitions. +``` +qry = str_glue( + "SELECT * + FROM {tablename} + WHERE subset IN ({subsets})" + ) +data_subset = dbGetQuery(con, qry) +``` +For each variable in the list vars=(c("height","weight","bmi","sercr","album","gfr_epi","heglb"), introduce a binary variable for whether the variable is NA (which means "missing") and a separate binary variable for whether it is out of bounds (that is, not missing but below the clinically plausible min or above the clinically plausible max as determined by UCSF clinicians). These boundaries are imported from [imputation_rules.xlsx](#imputation_rulesxlsx). The function **valueExceptions** returns a data frame with usrds_id (the key field) and binary values to indicate whether or not each column in vars is NA. +``` +valueExceptions = function(df, vars) { + bounds = read_excel(str_glue("{source_dir}imputation_rules.xlsx"), sheet = + "Bounds") %>% as.data.frame() + isnavars = c() + for (v in vars) { + newv = str_glue("wasna_{v}") + df[, newv] = as.integer(is.na(df[, v])) + isnavars = c(isnavars, newv) + } + outofbndsvars = c() + for (v in vars) { + newv = str_glue("outofbnds_{v}") + df[, newv] = as.integer(!is.na(df[, v]) & + !(df[, v] >= bounds[1, v] & + df[, v] <= bounds[2, v])) + outofbndsvars = c(outofbndsvars, newv) + } + return(df[, c("usrds_id", isnavars, outofbndsvars)]) +} +``` +Step 3. Execute **valueExceptions** +``` +labvars=c("height","weight","bmi","sercr","album","gfr_epi","heglb") +ve=valueExceptions(data_subset,labvars) +``` +Join the out of bounds binary indicators for each patient to the data. +``` +df=data_subset %>% + left_join( + ve, + by="usrds_id") +``` +Step 4. Create a function to set the values to NA if it is out of bounds. +``` +setOutOfBoundsToNA=function(df,vars) { + for (v in vars) { + df[,v]=ifelse( + df[,paste0("outofbnds_",v)] == 1, + NA, + df[,v]) + } + return(df) +} +``` +Step 5. Execute the above function on the data +``` +df=setOutOfBoundsToNA(df,labvars) +oobvars = setdiff(names(df),names(data_subset)) +``` +Step 6. Get the list of categorical features. +``` +getCategoryVars <- function(dataset){ + pattern1 = "^MEDCOV|^PATTXOP|^PATINFORMED$|^DIET|^NEPHCARE|^EPO" %>% tolower() + pattern2 = "^DIAL|^TYPTRN|^AVGMATURING|^AVFMATURING" %>% tolower() + pattern3 = "^ACCESSTYPE|^TRCERT|^CDTYPE" %>% tolower() + pattern4 = "^EMPCUR|^EMPPREV|^pdis$|^hispanic$|^COMO_" %>% tolower() + categoryVars = names(dataset)[grepl(pattern1, names(dataset))] + categoryVars = union(categoryVars, names(dataset)[grepl(pattern2, names(dataset))]) + categoryVars = union(categoryVars, names(dataset)[grepl(pattern3, names(dataset))]) + categoryVars = union(categoryVars, names(dataset)[grepl(pattern4, names(dataset))]) + return(categoryVars) +} + +categoryVars = getCategoryVars(df) +``` +Step 7. Get the list of continuous features +``` +getContinuousVars <- function(dataset){ + pattern_continuous = "^GFR_EPI|^SERCR|^ALBUM|^HEGLB|^HBA1C|^BMI$|^HEIGHT|^WEIGHT" %>% tolower() + continuousVars = names(dataset)[grepl(pattern_continuous, names(dataset))] + return(continuousVars) +} +continuousVars = getContinuousVars(df) +df = df[, c("usrds_id", + "subset", + "comorbid", + "inc_age", + "race", + "sex", + "disgrpc", + "waitlist_status", + "days_on_waitlist", + "died_in_90", + oobvars, + categoryVars, + continuousVars)] +``` +Step 8. Get non numeric features +``` +getNonNumericCols = function(dx) { + cols = c() + for (v in names(dx)) { + if (!is.numeric(dx[, v])) { + cols = c(cols, v) + } + } + return(cols) +} +nonNumCols = setdiff(getNonNumericCols(df), c("pdis", "comorbid", "cdtype","hispanic","waitlist_status")) +``` +ML models typically require numeric values instead of characters or factors. The function **replaceCharacterVals** ensures that character values are replaced with a number. +``` +replaceCharacterVals = function(dx, + vars, + sourceValue = c("N", "Y", "M", "F", "U", "C", "X", "D", "I", "A", "R"), + sinkValue = c("2", "1", "12", "13", "9", "15", "16", "17", "18", "20", "21")) +{ + for (v in vars) { + print(v) + dx[, v] = mapvalues(pull(dx, v), sourceValue, sinkValue) + dx[, v] = as.integer(pull(dx, v)) + } + return(dx) +} +df = replaceCharacterVals(df, nonNumCols) +``` +**pdis** must be encoded as a number prior to ML model training. +``` +recodePdis = function(df, con) { + df$pdis = df$pdis %>% + trimws() %>% str_pad(., + width = 7, + side = "right", + pad = "0") #Format pdis with the same padding as in pdis_recode_map + pdis_map = dbGetQuery( + con, " + SELECT pdis, cdtype, pdis_recode + FROM pdis_recode_map") + + pdis_map = pdis_map %>% + group_by(pdis, cdtype) %>% + dplyr::summarise(pdis_recode = min(pdis_recode)) + + df = df %>% + left_join( + pdis_map, + by = c("cdtype", "pdis")) %>% + mutate( + pdis_recode = ifelse(is.na(pdis_recode), 9999, pdis_recode) + ) + + return(df) +} +df = recodePdis(df, con) +``` +Step 9. Count value types in como_* variables for each ID +``` +comoEncode <- function(dataset){ + como_names = names(dataset)[grepl("^como_", names(dataset))] + + dataset$num_como_nas = apply( + dataset[, como_names], + 1, + function(xx) + sum(is.na(xx)) + ) + dataset$num_como_Ns = apply( + dataset[, como_names], + 1, + function(xx) + sum(xx == 2, na.rm = TRUE) + ) + dataset$num_como_Ys = apply( + dataset[, como_names], + 1, + function(xx) + sum(xx == 1, na.rm = TRUE) + ) + dataset$num_como_Us = apply( + dataset[, como_names], + 1, + function(xx) + sum(xx == 9, na.rm = TRUE) + ) + return(dataset) +} +df = comoEncode(df) +``` +Step 10. Remove features from the `medevid` table that are not needed for the ML models +- All comorbidities that are only captured on the 1995 Medical Evidence Form (and therefore before our cohort ESRD inicident years of 2008-2017), such as **como_cararr**, **como__hiv**, etc. +- All laboratory variables that have greater than 40% missingness, such as **albumin** and **hba1c** +- All year variables and masked dates, such as **incyear**, **masked_died**, etc. +- All **pdis** (primary disease causing ESRD) re-formatted variables, such as **pdis_count**, **pdis_mortality**, etc. +``` + varsToDelete = c( + "albumlm", + "como_ihd", + "como_mi", + "como_cararr", + "como_dysrhyt", + "como_pericar", + "como_diabprim", + "como_hiv", + "como_aids", + "comorbid_count", + "comorbid_mortality", + "comorbid_se", + "comorbid", + "ethn", + "hba1c", + "incyear", + "masked_died", + "masked_tx1fail", + "masked_txactdt", + "masked_txlstdt", + "masked_txinitdt", + "masked_remdate", + "masked_unossdt", + "masked_mefdate", + "masked_ctdate", + "masked_tdate", + "masked_patsign", + "masked_trstdat", + "masked_trnend", + "pdis_count", + "pdis_mortality", + "pdis_se", + "pdis", + "recnum", + "tottx" + ) +df[, varsToDelete] = NULL +``` +Step 11. Get the `preesrdfeatures` for the 2 subsets of data. +``` +qry = str_glue( + "SELECT * + FROM {table_preesrd} + WHERE subset in ({subsets})" + ) +preesrd = dbGetQuery(con, qry) +``` +Step 12. Join the data with columns from `preesrdfeatures` +``` +full_data = df %>% + left_join( + preesrd, + by = c("usrds_id","subset") + ) +``` +Step 13. Save to Postgres as `medxpreesrd` +``` +dbWriteTable( + con, + "medxpreesrd", + full_data, + row.names = FALSE + ) +``` + +## 6.2.19 Impute Missing Values + +Missing data have the potential to introduce bias and loss of information, which can result in invalid conclusions. Multiple imputation was chosen as the method to impute missing values over single imputation methods because it addresses the uncertainty about missing data by creating several plausible imputed datasets. +For this project, multiple imputation was performed on the clinical and laboratory values (height, weight, BMI , serum creatinine, serum albumin, hemoglobin, and GFR-EPI) using the MICE (multiple imputations by chained equations) library in R (version 3.13.0), the predictive mean matching methodology for the imputations, and 5 imputations (5 datasets) to achieve 95% relative efficiency. + +***Steps for running S7_makeImputations.R*** + +This script contains the code to create 5 imputations for missing data for each of the laboratory variables +``` +weight +height +gfr_epi +sercr +album +``` +and saves the data to Postgres as the `micecomplete_pmm` table. + +The table `micecomplete_pmm` has 5 rows per **usrds_id**, for each of the imputed columns. There is one row per imputation, hence 5 rows per **usrds_id**. A modeler who wants to use imputed values would use both `medxpreesrd` and `micecomplete_pmm`, replacing weight, height, bmi, sercr, etc. in `medxpreesrd` with the imputed values in `micecomplete_pmm`. This is shown in the modeling steps. + +**Input**: +- [imputation_rules.xlsx](#imputation_rulesxlsx) +``` +medxpreesrd +``` +**Output**: +``` +micecomplete_pmm +``` +Step 1. Define the function to import the data and impute the missing values and save to Postgres as a new table. The function: +- Sets out-of-bounds values to NA so that they will be imputed +- Lists the variables to impute +- Lists the variables used to inform the imputation +- Imputes the missing values +- Calculates BMI and GFR as they are derived from other imputed variables + +``` +makeImputations <- + function(con, subset, bounds, impseed, data_tablename) { + df = dbGetQuery( + con, + str_glue( + "SELECT * + FROM {data_tablename} + WHERE subset={subset}" + )) + + varstoimpute = names(bounds)[2:length(names(bounds))] + + varstoimpute = c( + "height", + "weight", + "bmi", + "sercr", + "album", + "gfr_epi", + "heglb" + ) + + varstouse = c( + "inc_age", + "race", + "sex", + "hispanic", + "num_como_nas", + "num_como_Ns", + "num_como_Ys", + "num_como_Us", + "sercr", + "height", + "weight", + "album", + "heglb" + ) + + dg = df[, c("usrds_id", union(varstoimpute, varstouse))] + dh = df[, c("usrds_id", "wasna_gfr_epi")] + dg = dg %>% + mutate( + hispanic = as.factor(hispanic), + race = as.factor(race), + sex = ifelse(is.na(sex), 0, sex) %>% + as.factor() + ) + + imp <- mice(dg, seed = impseed, maxit = 0) + predictorMatrixDf = imp$predictorMatrix + #An entry of 1 means the column variable was used to impute the row variable + meth = imp$method + + #row_imputed indexes the row (variable to be imputed); + #c indexes the column (variable to use as an independent variable to impute row_imputed) + for (row_imputed in colnames(predictorMatrixDf)) { + predictorMatrixDf[,row_imputed ] = 0 + } + + for (col_imputed in varstoimpute) { + for (impute_by in varstouse) { + if (col_imputed != impute_by) + predictorMatrixDf[col_imputed, impute_by] = 1 + } + } + + # bmi is arithmetically related to weight and height + # so it needs to be handled with a separate model + predictorMatrixDf["bmi", "height"] = 1 + predictorMatrixDf["bmi", "weight"] = 1 + + for (to_use in c("usrds_id", varstouse)) { + meth[to_use] = "" + } + for (to_impute in varstoimpute) { + meth[to_impute] = "pmm" + } + meth["bmi"] = "~ I(weight/(.01*height)^2)" + #Model the arithmetic relationship among bmi, weight, and height + + miceimp <- + mice( + dg, + m = 5, + maxit = 20, + threshold = .99999, + seed = impseed, + predictorMatrix = predictorMatrixDf, + method = meth, + print = FALSE + ) + + writeImputations( + con, + miceimp, + varstoimpute, + dh, + subset + ) + return(0) + +} +``` +Step 2. Set the seeds for each subset, import the boundary data. +``` +seeds = c(2397, 3289, 4323, 4732, 691, 2388, 2688, 176, 1521, 461) +source_dir = file.path("CreateDataSet") +bounds = read_excel(file.path(source_dir, "imputation_rules.xlsx"), sheet ="Bounds" + ) %>% as.data.frame() +``` +Step 3. Execute the makeImputations function for each subset of the data (according to the 10 partitions). +``` +for (s in 0:9) { + makeImputations( + con, + subset = s, + bounds, + impseed = seeds[s], + data_tablename="medxpreesrd" + ) +} +``` +***Points to consider*** + +1. Curating clinical and laboratory variables requires input from clinicians to remove outlier values and to properly identify relevant variables to retain in the training dataset. For example: +- Both hemoglobin and hematocrit are included in the USRDS data and are used by clinicians to identify anemia. Clinicans identified hemoglobin as the more accurate variable so hematocrit was removed from the training dataset since keeping both features results in redunant data. +- GFR-EPI is kept in the training data as it is perferred in clinical practice over GFR MDRD. + +2. For smaller datasets, all features in the training dataset can be used to inform the imputation. With a dataset of over 1 million observations, using all features is time prohibitive. Variables that are rarely missing and correlate with the variables to be imputed (age, race, sex, ethnicity, number of comorbidities, and the clinical/laboratory values) were used in the imputation model for this project. + +3. Variables like BMI and GFR should be passively imputed since they are derived from other imputed variables (BMI: height and weight, GFR: serum creatinine, along with age, sex, and race) + +4. There are various types of imputation methods that can be selected from the 'mice' package, such as 'norm', 'pmmm', etc. Running a goodness of the imputations test by masking and then imputing known values as well as comparing the runtimes for each method will help the user select the appropriate imputation method. + +5. Only the features that were missing at random (MAR) with a percent missingness <40% were imputed (i.e., clinical and laboratory values of height, weight, BMI , serum creatinine, serum albumin, hemoglobin, and GFR-EPI). Future researchers could improve the imputations by imputing features that are missing not at random (MNAR) with a more complex imputation model. Additionally other multiple imputation packages, such as the multiple imputation (MI) and Amelias packages, could also be used for the imputations. + +6. Storing imputations in a database table separate from the table storing medevid, patients, and pre-ESRD data prevents the rest of the training dataset from being stored five times; it also reduces the amount of storage required for the training dataset. + +## 6.2.20 Utility files + +### dx_mappings_ucsf.csv + +Utility file: Used by the script [S3d_dxCodeGrouping.R](#6214-diagnosis-groupings) to produce the table `dxMap` + +UCSF advised the categorizations of diagnosis codes. + +### 2017_I9gem_map.txt + +Utility file: Used by the script [S5_pdis_mapping.R](6217-map-icd9-to-icd10) to produce the table `pdis_recode_map` for mapping pdis codes between ICD-9 and ICD-10. + +### icd10_ccs_codes.R + +Utility file: Used by the script [S3d_dxCodeGrouping.R](#6214-diagnosis-groupings) to produce the table `dxMap` + +Group codes related to alcohol abuse, drug abuse, pulmonary disorders, and renal failure based on the Clinical classification system (CCS) rules for grouping ICD-10 diagnosis codes. + +See [https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp#download](https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp#download) + +### icd10_dx_codes.txt + +Utility file: Used by the script [S3d_dxCodeGrouping.R](#6214-diagnosis-groupings) to produce the table `dxMap` + +Group codes related to alcohol abuse, drug abuse, pulmonary disorders, and renal failure based on the Clinical classification system (CCS) rules for grouping ICD-10 diagnosis codes. + +See [https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp#download](https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp#download) + +### icd9_ccs_codes.R + +Utility file: Used by the script [S3d_dxCodeGrouping.R](#6214-diagnosis-groupings) to produce the table `dxMap` + +Group codes related to alcohol abuse, drug abuse, pulmonary disorders, and renal failure based on the Clinical classification system (CCS) rules for grouping ICD-9 diagnosis codes. + + See [https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp#download](https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp#download) + +### icd9_dx_2014.txt + +Utility file: Used by the script [S3d_dxCodeGrouping.R](#6214-diagnosis-groupings) to produce the table `dxMap` + +Group codes related to alcohol abuse, drug abuse, pulmonary disorders, and renal failure based on the Clinical classification system (CCS) rules for grouping ICD9 diagnosis codes. + +See [https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp#download](https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp#download) + +### imputation_rules.xlsx + +Utility used by script [S7-makeImputations.R](#6219-impute-missing-values) and [S6-prepareDataSet.R](#6218-prepare-data-for-modeling) + +Contains the upper and lower bounds for clinical and laboratory variables. + +Table: Upper and lower bounds for clinical and laboratory variables +|Variable |Lower bound |Upper bound| +|---------------|---------------|-----------| +|Height (cm) |76 |243| +|Weight (kg) |20 |250| +|BMI (kg/m2) |13 |75| +|Serum Creatinine (mg/dL) |0.5 |50| +|Serum Albumin (g/dL) |0.5 |8| +|GFR EPI |1 |30| +|Hemoglobin (g/dL) |2 |18| + +### pre_esrd_ip_claim_variables.R + +Utility: used by [S3a_esrd_claims.R](#6210-get-pre-esrd-claim-data) + +Filenames and column types to input into [S3a_esrd_claims.R](#6210-get-pre-esrd-claim-data) to create the table for the inpatient (IP) pre-ESRD claims from 2011-2017. Pre-ESRD claims in USRDS are kept in files specific to the year. The order of these filenames is very important as the 2012 table needs to be created first in the function. The .csv files named here are produced in script [S1a-convertSAStoCSV.R](#623-convert-data-to-csv) + +Filenames: +``` +"preesrd5y_ip_clm_inc2012" +"preesrd5y_ip_clm_inc2013" +"preesrd5y_ip_clm_inc2014" +"preesrd5y_ip_clm_inc2015" +"preesrd5y_ip_clm_inc2016" +"preesrd5y_ip_clm_inc2017" +"preesrd5y_ip_clm_inc2011" +``` + +### pre_esrd_hh_claim_variables.R + +Utility: used by [S3a_esrd_claims.R](#6210-get-pre-esrd-claim-data) + +File names and column types to input into [S3a_esrd_claims.R](#6210-get-pre-esrd-claim-data) to create the table for the home health (HH) pre-ESRD claims from 2011-2017. Pre-ESRD claims in USRDS are kept in files specific to the year. The order of these filenames is very important as the 2012 table needs to be created first in the function. The .csv files named here are produced in script [S1a-convertSAStoCSV.R](#623-convert-data-to-csv) + +Filenames: +``` +"preesrd5y_hh_clm_inc2012" +"preesrd5y_hh_clm_inc2013" +"preesrd5y_hh_clm_inc2014" +"preesrd5y_hh_clm_inc2015" +"preesrd5y_hh_clm_inc2016" +"preesrd5y_hh_clm_inc2017" +"preesrd5y_hh_clm_inc2011" +``` + +### pre_esrd_hs_claim_variables.R + +Utility: used by [S3a_esrd_claims.R](#6210-get-pre-esrd-claim-data) + +Filenames and column types to input into [S3a_esrd_claims.R](#6210-get-pre-esrd-claim-data) to create the table for the hospice (HS) pre-ESRD claims from 2011-2017. Pre-ESRD claims in USRDS are kept in files specific to the year. The order of these filenames is very important as the 2012 table needs to be created first in the function. The .csv files named here are produced in script [S1a-convertSAStoCSV.R](#623-convert-data-to-csv) + +Filenames: +``` +"preesrd5y_hs_clm_inc2012" +"preesrd5y_hs_clm_inc2013" +"preesrd5y_hs_clm_inc2014" +"preesrd5y_hs_clm_inc2015" +"preesrd5y_hs_clm_inc2016" +"preesrd5y_hs_clm_inc2017" +"preesrd5y_hs_clm_inc2011" +``` + +### pre_esrd_op_claim_variables.R + +Utility script: used by [S3a_esrd_claims.R](#6210-get-pre-esrd-claim-data) + +Filenames and column types to input into [S3a_esrd_claims.R](#6210-get-pre-esrd-claim-data) to create the table for the outpatient (OP) pre-ESRD claims from 2011-2017. Pre-ESRD claims in USRDS are kept in files specific to the year. The order of these filenames is very important as the 2012 table needs to be created first in the function. The .csv files named here are produced in script [S1a-convertSAStoCSV.R](#623-convert-data-to-csv) + +Filenames: +``` +"preesrd5y_op_clm_inc2012" +"preesrd5y_op_clm_inc2013" +"preesrd5y_op_clm_inc2014" +"preesrd5y_op_clm_inc2015" +"preesrd5y_op_clm_inc2016" +"preesrd5y_op_clm_inc2017" +"preesrd5y_op_clm_inc2011" +``` + +### pre_esrd_sn_claim_variables.R + +Utility: used by [S3a_esrd_claims.R](#6210-get-pre-esrd-claim-data) + +Filenames and column types to input into [S3a_esrd_claims.R](#6210-get-pre-esrd-claim-data) to create the table for the skilled nursing (SN) pre-ESRD claims from 2011-2017. Pre-ESRD claims in USRDS are kept in files specific to the year. The order of these filenames is very important as the 2012 table needs to be created first in the function. The .csv files named here are produced in script [S1a-convertSAStoCSV.R](#623-convert-data-to-csv). + +Filenames: +``` +"preesrd5y_sn_clm_inc2012" +"preesrd5y_sn_clm_inc2013" +"preesrd5y_sn_clm_inc2014" +"preesrd5y_sn_clm_inc2015" +"preesrd5y_sn_clm_inc2016" +"preesrd5y_sn_clm_inc2017" +"preesrd5y_sn_clm_inc2011" +``` + +### pre_esrd_pre2011_claim_variables.R + +Utility: used by [S3c_esrd_claims_pre_2011.R](#6213-get-pre-2011-pre-esrd-claim-data) + +Filenames and column types to input into [S3c_esrd_claims_pre_2011.R](#6213-get-pre-2011-pre-esrd-claim-data) to create the tables for the pre-ESRD claims from 2008-2010. Pre-2011 pre-ESRD claims in USRDS are kept in files specific to the year but not claim type. The .csv files named here are produced in script [S1a-convertSAStoCSV.R](#623-convert-data-to-csv). + +Filenames: +``` +inc2010 +inc2009 +inc2008 +``` + +### setfieldtypes.R + +Used by the script [S4a_pre_esrd_full.R](#6215-aggregate-pre-esrd-claim-data) + +Utility file: The "setfieldtypes" utility is used in order to cast these column types explicitly, thereby avoiding auto assignment of "integer64" as the column data type. + +## 6.2.21 Documentation of the Training Dataset + +All features included in the training dataset are documented in the data dictionary. Features fall into two main categories: +- Features taken directly from USRDS +- Features constructed from the data in USRDS + +Additionally, all features in the training dataset are labeled as operational or not operational to identify and flag "nuisance variables" in the training dataset to ensure the that the ML models do not learn on noise. + +# 6.3 ML Modeling and Evaluation + +Three ML algorithms were selected to provisionally test the training dataset: eXtreme gradient boosting (XGBoost), logistic regression (LR), and a neural network implementation--multilayer perceptron (MLP). Some of the general considerations for selecting an algorithm include characteristics of the training dataset (tabular data vs image data, number of features, etc.), algorithms that have performed well in a specific domain area (kidney disease/clinical use cases), and available computational resources (for example, deep learning algorithms require intense compute resources). The algorithms that were selected are a mixture of non-parametric (XGBoost) and parametric (LR and MLP) models. +- XGBoost is a popular implementation of gradient boosted decision trees because it performs especially well for tabular data, can be applied to a wide array of use cases, data types, and desired prediction outcomes (regression vs classification), and can handle missing value natively which allows for a comparison between models run on non-imputed data versus models run on imputed data. +- LR is a classic categorization model that can be used to examine the association of (categorical or continuous) independent variable(s) with one binary dependent variable. However, it requires that the input dataset have no missing values. +- MLP is a class of hierarchical artificial neural network (ANN) that consists of at least three layers of nodes – an input layer, a hidden layer and an output layer – to carry out the process of machine learning. They are used for tabular datasets and classification prediction problems. + +The training dataset derived from the raw USRDS dataset was developed by building features relevant to the use case – predicting mortality in the first 90 days of dialysis. Each feature captures information known about a patient on or prior to the date of dialysis initiation. The final structure of the training dataset, which was used to train and test the ML models, consists of approximately 200 features, and has one record per patient. Two sets of features were included in the training dataset: features taken directly from the USRDS datasets and features that were constructed. The training dataset with the full set of features were split into train and test at approximately a 70/30 ratio. + +Each section below contains the code for pre-processing the data, hyperparameter tuning, final modeling, calibration, fairness assessment, and risk assessment for each model. All models were evaluated using area under the receiver operating characteristic curve (AUC ROC) and confusion matrix (true positives, true negatives, false positives, and false negatives) evaluated at thresholds from 0.1 to 0.5. +![ml_models_detailed](./images/ONC_implementation_guide_modeling.png) + +***Points to Consider*** + +The 90-day mortality outcome was predicted using USRDS data available from patients on or prior to being diagnosed with ESRD, who progressed to ESRD. This means that the ML models predicted an outcome conditional on ESRD (e.g., the models are applicable only to those having ESRD). Future extensions of this work could merge USRDS data with EHR data to be able to predict progression to ESRD or incorporate patient-centered features from EHR data to better predict mortality in the first 90 days after dialysis initiation + +## 6.3.1 Non-Imputed XGBoost Model + +All results for the non-imputed XGBoost model are located in the /roc_auc/ directory. + +**Environment** + +The environment used for the non-imputed XGBoost model was purchased on Amazon Web Services (AWS): +``` +Name: m5.4xlarge +vCPU: 16 +GPU: 0 +Cores: 8 +Threads per core: 2 +Architecture: x86_64 +Memory: 64 GB +Operating System: Linux (Ubuntu 20.04.1 Focal Fossa) +Network Performance: 10 GB or less +Zone: US govcloud west +``` + +All sections of code for the non-imputed XGBoost model takes approximately 2 days to run. + +The XGBoost models were created using R (version 3.6.3 (2020-02-29)) and the following libraries: +|R library |Version | +|-------------------|---------| +|RPostgres | 1.3.1| +|DBI |1.1.1| +|stringr| 1.4.0| +|haven |2.4.0| +|readr |1.4.0| +|lubridate| 1.7.9.2| +|dplyr |1.0.4| +|magrittr| 1.5| +|tidyr |1.1.2| +|sqldf |0.4-11| +|RSQLite| 2.2.3| +|gsubfn |0.7| +|proto |1.0.0| +|readxl |1.3.1| +|plyr |1.8.6| +|skimr| 2.1.2| +|data.table| 1.14.0| +|mltools |0.3.5| +|here | 1.0.1| +|rgenoud| 5.8-3.0| +|DiceKriging| 1.5.8| +|purrr |0.3.4| +|mlrMBO |1.1.5| +|mlr |2.18.0| +|smoof| 1.6.0.2| +|checkmate| 2.0.0| +|ParamHelpers| 1.14| +|xgboost |1.3.2.1| +|Matrix |1.2-18| +|rBayesianOptimization| 1.1.0| +|rsample |0.0.9| +|pROC |1.17.0.1| +|openxlsx| 4.2.3| + + +### 6.3.1.1 Pre-processing the training dataset +XGBoost can only handle numeric values as inputs to the model and can natively handle missing values - so, all categorical variables were one-hot encoded into dummy variables that are binary indicators of each factor in the categorical features (e.g., the sex feature will be turned into 3 columns: sex_1 (male), sex_2 (female), sex_3 (unknown)). + +***Steps for running the 0_xgb_nonimputed_preprocess.R script*** + +- Inputs: + + `medexpressesrd`table from the Postgres database + + category_variables.R +- Outputs: + + universe.RData (data ready for modeling) + +Step 1. Load the libraries +``` +library(RPostgres) #Interface to PostgreSQL +library(DBI) #R database interface +library(dplyr) +library(tidyr) +library(skimr) #Summarizing databases +library(data.table) +library(mltools) #data.table and mltools are needed for the "one_hot" function +library(readr) #read rds +``` +Step 2. Load the list of categorical variables +``` +source(file.path("~","ONC_xgboost","category_variables.R")) +``` +Step 3. Connect to the Postgres database and load the `medexpressesrd` table as the variable `universe` + +The credentials required to connect to the database should be inserted in the following snippet of code below: +``` +con <- dbConnect( + RPostgres::Postgres(), + dbname = '', + host = '', + port = '', + user = '', + password = '') +``` +The data from the database is loaded into R as the variable `universe`. +``` +universe=dbGetQuery( + con, + "SELECT * + FROM medxpreesrd") +``` + +Step 4. Set numeric features as numeric type +``` +num_vars = setdiff(names(universe) , categoryVars) + +continuous_vars = c("height", "weight", "bmi", "sercr", "album", "gfr_epi", "heglb") + +num_vars = setdiff(num_vars, continuous_vars) +for (cc in num_vars) { + universe[,cc]=as.numeric(universe[,cc]) +} +``` +Step 5. Separate categorical features from continuous and numeric to one-hot encode +``` +for (c in categoryVars) { + universe[,c]=as.factor(universe[,c]) +} +universe=data.table(universe) +universe=one_hot(as.data.table(universe), naCols=TRUE, dropUnusedLevels = TRUE) +``` +Step 6. Save the pre-processed data +``` +save(universe, file="universe.RData") +``` +***Points to consider*** + +One-hot encoding the categorical variables is preferable to numeric encoding (casting categorical encodings as numeric) as it is a better numeric representation of ordinal variables. However, one-hot encoding increases the number of variables in the training dataset which increases run time. + +### 6.3.1.2 Hyperparameter tuning for the non-imputed dataset +Hyperparameters were tuned on the training partitions for the non-imputed dataset with a Bayesian optimization and 5-fold cross validation to identify the optimal hyperparameters for the model. Bayesian optimization is the preferred method for hyperparameter tuning (versus grid search or random search) because it is able to find a set of hyperparameters that result in model performance equivalent to what the model performance would have been if the optimal hyperparameters had been found through exhaustive grid search. Exhaustive grid search can require testing tens of thousands of hyperparameter sets, which could be computationally infeasible or take an extraordinary amount of time. Because Bayesian optimization tests the combinations in an informed appoach, it is often able to find an optimal set of hyperparameters in only 50-100 iterations. + +The best performing model was evaluated by the selecting the hyperparameter combination with the highest AUC ROC. + +***Steps for running the 1_xgb_nonimputed_cv.R script*** + +- Input: + + universe.RData +- Output: + + 2021_xgb_cv_results_nonimputed.RData + +Step 1. Load the libraries +``` +library(RPostgres) +library(DBI) +library(xgboost) +library(dplyr) +library(tidyr) +library(magrittr) +library(smoof) +library(mlrMBO) # for bayesian optimisation +library(skimr) # for summarizing databases +library(purrr) # to evaluate the loglikelihood of each parameter set in the random grid search +library(DiceKriging) +library(rgenoud) +library(here) +library(data.table) +library(mltools) #data.table and mltools are needed for "one_hot" function +library(readr) #read rds +``` + +Step 2. Load the one hot encoded data and keep only the training subsets (1-6) and separate dependent variable and non-feature columns used for identification or subsetting +``` +load('universe.RData') + +depvar = "died_in_90" +trainsubsets = c(0,1,2,3,4,5,6) + +rhscols = setdiff(names(universe), c("usrds_id", "subset", "died_in_90")) + +train_onc=universe %>% filter(subset %in% trainsubsets) %>% as.data.frame() +``` +Step 3. Generate the list of indices for 5-fold cross validation +``` +# creating 5 fold validation +cv_folds = rBayesianOptimization::KFold(train_onc[, depvar], + nfolds= 5, + stratified = TRUE, + seed= 0) +``` +Step 4. Prepare the training dataset as a matrix +``` +dtrain <-xgb.DMatrix(as.matrix(train_onc[, rhscols]), label = train_onc[, depvar]) +``` +Step 5. Define the parameters for hyperparameter tuning + +The hyperparameters that were selected for tuning and the ranges that were tuned were: +|Parameter |Range | +|-------------------|---------| +|Eta | 0.001 - 0.8 | +|Gamma | 0 - 9 | +|Lambda | 1 -9 | +|Alpha | 0 - 9 | +|Max Depth | 2 - 10 | +|Minimum Child Weight | 1 - 5 | +|Number of Rounds | 10 - 500 | +|Subsample | 0.2 - 1 | +|Column Sample by Tree | 0.3 - 1 | +|Maxiumum Bin | 255 - 1023 | + +Additional information on these hyperparameters can be found at https://xgboost.readthedocs.io/en/latest/. + +Parameters that were set include: +- scale_pos_weight as 3.5, which is the square root of the ratio of the negative class (survived the first 90 days of dialysis) and the positive class (died in the first 90 days of dialysis). This parameter handles the class imbalance by weighting the minority class (died in the first 90 days of dialysis). +- Number of iterations as 100. Bayesian optimization will run through 100 iterations to identify the optimal hyperparameters. +- Early stopping rounds to 15, as evaluated using the highest AUC ROC. This parameter ends model training if the AUC ROC has not increased in 15 iterations. + +``` +# Tune parameters --------------------------------------------------- +obj.fun <- smoof::makeSingleObjectiveFunction( + name = "xgb_cv_bayes", + fn = function(x){ + set.seed(12345) + cv <- xgb.cv(params = list( + booster = "gbtree", + scale_pos_weight = sqrt(12), + eta = x["eta"], + max_depth = x["max_depth"], + min_child_weight = x["min_child_weight"], + gamma = x["gamma"], + lambda = x["lambda"], + alpha = x["alpha"], + subsample = x["subsample"], + colsample_bytree = x["colsample_bytree"], + max_bin = x["max_bin"], + objective = 'binary:logistic', + eval_metric = "auc", + tree_method = "hist"), + data=dtrain, + nrounds = x["nround"], + folds = cv_folds, + prediction = FALSE, + showsd = TRUE, + early_stopping_rounds = 15, + verbose = 1) + + cv$evaluation_log[, max(test_auc_mean)] + }, + par.set = makeParamSet( + makeNumericParam("eta", lower = 0.001, upper = 0.8), + makeNumericParam("gamma", lower = 0, upper = 9), + makeNumericParam("lambda", lower = 1, upper = 9), + makeNumericParam("alpha", lower = 0, upper = 9), + makeIntegerParam("max_depth", lower = 2, upper = 10), + makeIntegerParam("min_child_weight", lower = 1, upper = 5), + makeIntegerParam("nround", lower = 10, upper = 500), + makeNumericParam("subsample", lower = 0.2, upper = 1), + makeNumericParam("colsample_bytree", lower = 0.3, upper = 1), + makeIntegerParam("max_bin", lower = 255, upper = 1023) + ), + minimize = FALSE +) + +des = generateDesign(n=length(getParamSet(obj.fun)$pars)+1, # the number of experiments cannot equal the number of variables therefore to increase computation time, we are adding 1 to the total number of hyperparameters. + par.set = getParamSet(obj.fun), + fun = lhs::randomLHS) ## . If no design is given by the user, mlrMBO will generate a maximin Latin Hypercube Design of size 4 times the number of the black-box function's parameters. + +control = makeMBOControl() +control = setMBOControlTermination(control, iters = 100) # number of Bayesian iterations +``` +Step 6. Tune the hyperparameters with Bayesian optimization and 5-fold cross-validation on the training data +``` +results = mbo(fun = obj.fun, + design = des, + control = control, + show.info = TRUE) +``` +Step 7. Save the results to the output file +``` +save(results, file = "2021_xgb_cv_results_nonimputed.RData") +``` +***Points to consider*** + +1. Benchmark tests should be run on a fewer number of iterations to gauge the run-time per iteration. Hyperparameter tuning in a model with a large hyperparameter space, such as for gradient boosted decision trees, can be computationally and time intensive. This approach allows the user to estimate the time to completion for the hyperparameter tuning script. + +2. Different AUC evaluation metrics can be chosen to determine the optimal set of hyperparameters, such as optimizing on precision-recall (PR) AUC or model calibration. The decision for the metric on which to optimize should be made in conjunction with clinical experts and depend on the goals of the model or study. + +### 6.3.1.3 Final XGBoost model for the non-imputed dataset + +***Steps for running the 2_xgb_nonimputed_final_model.R script*** + +The final model is trained on the training subsets (0-6) of all 5 sets of non-imputed data using the optimal hyperparameters from hyperparameter tuning. The final model is evaluated on the testing subsets (7-9) of all 5 sets of non-imputed data using the ROC AUC as well as on the confusion matrix (true positives, false positives, true negatives, and false negatives) and associated model evaluation metrics (sensitivity, specificity, positive predictive value, positive likelihood ratio, and F1 score) at 0.1-0.5 thresholds. + +- Input: +``` +universe.RData +2021_xgb_cv_results_nonimputed.RData +``` +- Output: +``` +[date]_xgbResults_onehot_nonimp.csv +2021_xgb_nonimputed_feature_importance.RData +2021_xgb_nonimputed_y_proba.csv +2021_nonimputed_predictions.xlsx +2021_xgbResults_nonimputed.csv +``` +Step 1. Load the libraries +``` +library(xgboost) +library(sqldf) +library(dplyr) +library(tidyr) +library(magrittr) +library(smoof) +library(mlrMBO) # for bayesian optimisation +library(skimr) # for summarising databases +library(purrr) # to evaluate the loglikelihood of each parameter set in the random grid search +library(DiceKriging) +library(rgenoud) +library(here) +library(data.table) +library(mltools) #data.table and mltools are needed for "one_hot" function +library(readr) #read rds +``` +Step 2. Load the one-hot encoded data and split the training subsets (0-6) from the test subsets (7-9) and separate dependent variable and non-feature columns used for identification or subsetting. +``` +load("universe.RData") + +depvar = "died_in_90" + +trainsubsets = c(0,1,2,3,4,5,6) +testsubsets = c(7,8,9) + +rhscols = setdiff(names(universe), c("usrds_id", "subset", "died_in_90")) + +train_onc=universe %>% filter(subset %in% trainsubsets) %>% as.data.frame() +train_onc = train_onc[order(train_onc$usrds_id),] + +test_onc=universe %>% filter(subset %in% testsubsets) %>% as.data.frame() +test_onc = test_onc[order(test_onc$usrds_id),] +``` +Step 3. Set the train and test datasets in the matrix format +``` +dtrain <-xgb.DMatrix(as.matrix(train_onc[, rhscols]), label = train_onc[, depvar]) +dtest <-xgb.DMatrix(as.matrix(test_onc[, rhscols]), label = test_onc[, depvar]) + +``` +Step 4. Set the seed and load in the optimal hyperparameters identified during hyperparameter tuning +``` +set.seed(297) + +load("./roc_auc/2021_xgb_cv_results_nonimputed.RData") + +xeta= results$x[['eta']] +xgamma= results$x[['gamma']] +xlambda= results$x[['lambda']] +xalpha= results$x[['alpha']] +xmax_depth= results$x[['max_depth']] +xmin_child_weight= results$x[['min_child_weight']] +xnround=results$x[['nround']] +xsubsample= results$x[['subsample']] +xcolsample_bytree= results$x[['colsample_bytree']] +xmax_bin=results$x[['max_bin']] + +scenarios = as.data.frame( + rbind( + c(xalpha, xcolsample_bytree, xeta, xgamma, xlambda, xmax_bin, xmax_depth, xmin_child_weight, xnround, xsubsample) + )) +names(scenarios)=c("alpha","colsample_bytree","eta","gamma","lambda","max_bin","max_depth", + "min_child_weight","rounds","subsample") + +scenarios$inx = 1:dim(scenarios)[1] + +watchlist <- list(eval = dtest, train = dtrain) + +attr(dtrain, 'label') <- getinfo(dtrain, 'label') +dy = NULL + +for (i in scenarios$inx) { + s = scenarios[scenarios$inx == i, ] + + param <- + list( + max_depth = s$max_depth, + eta = s$eta, + nthread = 16, + verbosity = 0, + gamma = s$gamma, + lambda = s$lambda, + alpha = s$alpha, + maximize = TRUE, + tree_method = "hist", + max_bin = s$max_bin, + min_child_weight=s$min_child_weight, + eval_metric = "auc", + colsample_bytree=s$colsample_bytree, + subsample=s$subsample, + scale_pos_weight=sqrt(12), + objective = "binary:logistic" + ) + ``` + Step 5. Set the seed and run the final non-imputed XGBooost model + ``` + set.seed(297) + starttime = proc.time()[3] + fit <- + xgb.train( + param, + dtrain, + s$rounds, + # nthread=16, + watchlist, + maximize = TRUE, + early_stopping_rounds = 15, + verbose = 1 + ) + ``` + Step 6: Obtain feature importance and save the file + ``` + feature_imp = xgb.importance(fit$feature_names, + model = fit) + + save(feature_imp, file = "./roc_auc/2021_xgb_nonimputed_feature_importance.RData") + ``` + Step 7. Save the predictions + ``` + dx = as.data.frame(cbind(predict(fit, newdata = dtest), as.vector(getinfo(dtest, "label")))) + names(dx)[1:2] = c("score", "y") + dx$usrds_id = test_onc$usrds_id + + write.csv(dx,file="./roc_auc/2021_xgb_nonimputed_y_proba.csv") + + openxlsx::write.xlsx(as.data.frame(dx), file = "./roc_auc/2021_nonimputed_predictions.xlsx", + sheetName='Sheet1', row.names=FALSE,showNA = F) + + ``` + Step 8. Calculate the confusion matrix by each threshold value + ``` + outdata = as.data.frame(seq(0, .99, .01)) + names(outdata) = "bin" + + above_thresh = sqldf( + "select a.bin as threshold, sum(b.y) as tp, count(b.y) as detections + from outdata a + left join dx b on a.bin<=b.score + group by a.bin + order by a.bin desc" + ) + + below_thresh = sqldf( + "select a.bin as threshold, sum(b.y) as fn, count(b.y) as nondetections + from outdata a + left join dx b on a.bin>b.score + group by a.bin + order by a.bin desc" + ) + + perfdata = above_thresh %>% left_join(below_thresh, by = c("threshold")) + perfdata$tp = replace_na(perfdata$tp, 0) + perfdata$fn = replace_na(perfdata$fn, 0) + + perfdata = perfdata %>% mutate( + fp = detections - tp, + tn = nondetections - fn, + sensitivity = tp / (tp + fn), + specificity = tn / (fp + tn), + fpr = 1 - specificity, + tpr = sensitivity, + LR = sensitivity / (1 - specificity), + ppv = tp / detections, + npv = tn / (tn + fn) + ) + + perfdata$iter = i + + perfdata$durationinsecs = durationinsecs + + replace = T) > sample(neg.scores, 7000, replace = T)) + perfdata$auc_xgb_test = max(fit$evaluation_log$eval_auc) + perfdata$auc_xgb_train = max(fit$evaluation_log$train_auc) + + dy = as.data.frame(rbind(dy, perfdata)) + + print(paste0("Finished iteration ", i, " auc_tim_test: ", max(perfdata$auc_xgb_test, " Duration ", durationinsecs))) +} + +dy = dy %>% mutate( + accuracy = (tp + tn) / (tp + tn + fp + fn), + f1_score = 2 * ppv * sensitivity / (ppv + sensitivity) +) +``` +Step 9. Save the results file +``` +write.csv(dy,file="./roc_auc/2021_xgbResults_nonimputed.csv") +``` + +### 6.3.1.4 Calibration +The calibration curve shows the reliability of the model by each prediction score category, the number of patients that fall into each category, and the proportion of patients in each category who actually died in the first 90 days following dialysis initiation. + +***Steps for running the 3_xgb_nonimputed_calibration.ipynb script*** + +- Input: +``` +2021_xgb_nonimputed_y_proba.csv +medexpreesrd +``` +- Output: +``` +xgb_nonimputed_calibrated.pickle +y_calibrated_xgb_nonimputed.pickle +``` + +Step 1. Import libraries +``` +import pandas as pd +import numpy as np +import pickle + +import sys +#path to the functions file +sys.path.append('../../onc_functions/') + +# import custom functions +from plot_functions import onc_plot_calibration_curve +from calibrate_onc import calibrate_onc + +#connect to posgres database +import psycopg2 +import sqlalchemy +from sqlalchemy import create_engine + +con = create_engine('postgresql://username:password@location/dbname') +``` +Step 2. Load results from the XGBoost non-imputed model +``` +pred_df = pd.read_csv('./roc_auc/2021_xgb_nonimputed_y_proba.csv') +``` +Step 3. Plot the calibration of the original model. This function **onc_plot_calibration_curve** is located in the /onc_functions/plot_functions.py file. +``` +def onc_plot_calibration_curve(y_true, y_proba, label, filename): + + #calculate numbers to plot + clf_score = brier_score_loss(y_true, y_proba, pos_label=1) + fraction_of_positives, mean_predicted_value = \ + calibration_curve(y_true, y_proba, n_bins=10) + # set up plot + fig1 = plt.figure(1, figsize=(10,10))#,dpi=400) + ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2) + ax2 = plt.subplot2grid((3, 1), (2, 0)) + + #plot the reference for a prefectly calibrated model + ax1.plot([0, 1], [0, 1], "k:", label="Reference Line") + + # plot the calibration curve + ax1.plot(mean_predicted_value, fraction_of_positives, "ks-", + label=label) + + # plot histogram of predicted values + ax2.hist(y_proba, range=(0, 1), bins=10, label=label, + histtype="step", lw=2) + + # set axes and other figure parameters + ax1.set_ylabel("Observed Event Rate") + ax1.set_xlabel("Predicted Event Rate") + ax1.set_ylim([-0.05, 1.05]) + ax1.legend(loc="lower right") + + ax2.set_xlabel("Mean predicted value") + ax2.set_ylabel("Count") + ax2.legend(loc="upper right", ncol=1) + + plt.rc('axes', labelsize=22) # fontsize of the x and y labels + plt.rc('xtick', labelsize=15) # fontsize of the tick labels + plt.rc('ytick', labelsize=15) # fontsize of the tick labels + plt.rc('legend', fontsize=20) # legend fontsize + #save figure resolution + plt.savefig(filename + ".png", dpi=400, transparent=True) + plt.show() +``` +Run the function above. +``` +onc_plot_calibration_curve( + y_true=data.y, + y_proba=data.score, + label='XGBoost non-imputed', + filename='./roc_auc/xgb_nonimputed_orig_calibration') +``` + +![xgb_nonimputed_orig_calibration.png](./images/xgb_nonimputed_orig_calibration.png) + +The XGBoost model can be calibrated by training an isotonic regression on a portion of the testing set. (Model calibration is performed as probabilities of death in the first 90 days are more informative and useful for clinicians than a simple binary prediction. In order to produce valid probability estimates, predicted events rates should track observed rates across the full range of predicted risk.) + +Step 4. Load the subset for each ID: +``` +df = pd.read_sql_query(''' +SELECT usrds_id, subset FROM medxpreesrd;''', con) +``` +Merge the subset details with the predictions. +``` +data = pd.merge(pred_df, df, how="left", on="usrds_id") +``` +The next steps are inside function **calibrate_onc** located in the /onc_functions/calibrate_onc.py file. + +Step 5. Split the predictions from the test set (how we evaluated the model) into a test/train for the calibration (isotonic regression classifier). Split test data (subsets 7-9) into new train (7-8)/test (9) sets +``` +calibration_train_set = data[((data.subset==7)|(data.subset==8))].copy() +calibration_test_set = data[data.subset==9].copy() +``` +Step 6. Define the calibration model +``` +ir = IsotonicRegression(out_of_bounds="clip") +``` +Step 7. Fit the model to the XGBoost predictions from the (new) training set +``` +ir.fit(calibration_train_set.score, calibration_train_set.y) +``` +Step 8. Evaluate the model using the (new) test set +``` +p_calibrated = ir.transform(calibration_test_set.score) +calibration_test_set['p_calibrated'] = p_calibrated +``` +Step 9. Save +``` +with open(path + 'model_calibrated_' + model_name + '.pickle', 'wb') as picklefile: + pickle.dump(ir,picklefile) + +with open(path + 'y_calibrated_' + model_name + '.pickle', 'wb') as picklefile: + pickle.dump(calibration_test_set, picklefile) +``` +Step 10. Print the scores from the original and calibrated model. The function **print_calibrated_results** is found in the /onc_functions/calibrate_onc.py file. +``` +def print_calibrated_results(y_true, y_pred, y_calibrated): + '''print scores for pre and post calibration''' + + acc = accuracy_score(y_true, np.round(y_pred)) + acc_calibrated = accuracy_score(y_true, np.round(y_calibrated )) + print ("accuracy - original/calibrated:", acc, "/", acc_calibrated) + + auc = roc_auc_score(y_true, y_pred) + auc_calibrated = roc_auc_score(y_true, y_calibrated) + print ("ROC AUC - original/calibrated: ", auc, "/", auc_calibrated) + + pr = average_precision_score(y_true, y_pred) + pr_calibrated = average_precision_score(y_true, y_calibrated ) + print ("avg precision - original/calibrated:", pr, "/", pr_calibrated) + + clf_score = brier_score_loss(y_true, y_calibrated, pos_label=1) + print("\tBrier: %1.3f" % (clf_score)) +``` +Run these 2 calibration functions +``` +calibrated_results = calibrate_onc(data, path='./roc_auc/', model_name='xgb_nonimputed') +``` +### 6.3.1.5 Plotting calibrated results +***Steps for running the 4_xgb_nonimputed_calibrated_plots.ipynb script*** + +- Input: +``` +y_calibrated_xgb_nonimputed.pickle +``` +- Output: +``` +xgb_nonimputed_calibration.png +xgb_nonimputed_mortality_bar.png +xgb_nonimputed_roc_auc_bw.png +2021_xgb_nonimputed_calibrated_confusion_matrix.csv +``` + +Step 1. Import libraries +``` +import pandas as pd +import numpy as np +import pickle + +import sys +#add the absolute path to the onc_functions directory +sys.path.append('../../onc_functions/') + + +#import custom plotting functions +from plot_functions import (onc_plot_calibration_curve, + onc_calc_cm, + onc_plot_roc, + onc_plot_precision_recall, + onc_plot_risk, + onc_plot_roc_no_threshold) +``` +Step 2. Load results from the calibrated model +``` +with open('./roc_auc/y_calibrated_xgb_nonimputed.pickle, 'rb') as picklefile: + calibrated_results = pickle.load(picklefile) +``` +Step 3. Plot the calibration curve of the calibrated model using the same **onc_plot_calibration_curve** function from /onc_functions/plot_functions.py +``` +onc_plot_calibration_curve( + y_true=calibrated_results.y, + y_proba=calibrated_results.p_calibrated, + label='XGBoost_non-imputed calibrated', + filename='./roc_auc/xgb_nonimputed_calibrated') +``` +![xgb_nonimputed_calibration.jpeg](./images/xgb_nonimputed_calibration.jpeg) + +Step 4. Plot the Risk of the calibrated model. This function **onc_plot_risk** is located and imported from /onc_functions/plot_functions.py +``` +def onc_plot_risk(y_true, y_proba, label, filename): + # calculate values for plot + fraction_of_positives, mean_predicted_value = \ + calibration_curve(y_true, y_proba, n_bins=10) + + # set up figure params + fig1 = plt.figure(1, figsize=(12,30),dpi=400) + ax1 = plt.subplot2grid((7, 1), (0, 0), rowspan=2) + + # bar plot + xs = np.arange(len(fraction_of_positives)) + ax1.bar(xs, mean_predicted_value, color='k', width = 0.25, label=label) + ax1.bar(xs+.25, fraction_of_positives, color='gray', width = 0.25, label='Observed') + + #more figure settings + plt.xticks(xs, np.arange(1, len(xs)+1, 1)) + ax1.set_ylabel("Mortality Rate") + ax1.set_xlabel("Decile of Predicted Mortality Risk") + ax1.legend(loc="upper left") + plt.rc('axes', labelsize=22) # fontsize of the x and y labels + plt.rc('xtick', labelsize=15) # fontsize of the tick labels + plt.rc('ytick', labelsize=15) # fontsize of the tick labels + plt.rc('legend', fontsize=20) # legend fontsize + #save plot + plt.savefig(filename + ".png", dpi=400, transparent=True) +``` +``` +onc_plot_risk( + calibrated_results.y, + calibrated_results.p_calibrated, + label='Predicted (XGBoost Non-Imputed)', + path='./roc_auc/', + filename='xgb_nonimputed_mortality_bar') +``` +![xgb_nonimputed_mortality_bar.jpeg](./images/xgb_nonimputed_mortality_bar.jpeg) + +Step 5. Plot the ROC AUC of the calibrated model. This function **onc_plot_roc** is located and imported from /onc_functions/plot_functions.py +``` +def onc_plot_roc(y_true, y_pred, model_name, **kwargs): + ''' + Plot the ROC AUC and return the test ROC AUC results. + INPUT: y_true, y_pred, model_name, **kwargs + ''' + + #calc values for plot + false_positives, true_positives, threshold = roc_curve(y_true, y_pred) + c_roc_auc_score = auc(false_positives, true_positives) + + #set figure params + fig1 = plt.figure(1, figsize=(12,30),dpi=400) + ax1 = plt.subplot2grid((7, 1), (0, 0), rowspan=2) + + #plot reference line for chance + ax1.plot([0, 1], [0, 1], linestyle='--', lw=2, color='gray', + label='Chance', alpha=.8) + + # plot AUC ROC + ax1.plot(false_positives, true_positives, + label=r'ROC (AUC = %0.3f)' % (c_roc_auc_score), + lw=2, alpha=.8, color = 'k') + + # additional figure params + ax1.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],) + ax1.legend(loc="lower right") + plt.xlabel('1-Specificity') + plt.ylabel('Sensitivity') + plt.rc('axes', labelsize=22) # fontsize of the x and y labels + plt.rc('xtick', labelsize=15) # fontsize of the tick labels + plt.rc('ytick', labelsize=15) # fontsize of the tick labels + plt.rc('legend', fontsize=20) # legend fontsize + # save plot + plt.savefig(model_name + "_calibrated_roc_auc_bw.png", dpi=400, transparent=True) + plt.show() +``` +``` +onc_plot_roc( + calibrated_results.y, + calibrated_results.p_calibrated, + model_name='xgb_nonimputed'); +``` +![xgb_nonimputed_roc_auc_bw_nothreshold_hires_crop.png](./images/xgb_nonimputed_roc_auc_bw_nothreshold_hires_crop.png) + +Step 6. Save the performance metrics at multiple thresholds. The following function is imported from /onc_functions/plot_functions.py +``` +def onc_calc_cm(y_true, y_predictions, range_probas=[0.1,0.5]): + ''' + Plot the confusion matrix and scores for multiple thresholds + ''' + df = pd.DataFrame(index = range_probas, + columns=['threshold','sensitivity','specificity', + 'likelihood_ratio_neg','likelihood_ratio_pos', + 'tp','fp','tn','fn','total_survived','total_deceased',]) + for proba_threshold in range_probas: + + cm = confusion_matrix(y_true, y_predictions > proba_threshold) + tn = cm[0][0] + fp = cm[0][1] + + sensitivity = recall_score(y_true, y_predictions > proba_threshold) + specificity = tn / (tn + fp) + + df.loc[proba_threshold, "threshold"] = proba_threshold + df.loc[proba_threshold,"sensitivity"] = sensitivity + df.loc[proba_threshold, "specificity"] = specificity + df.loc[proba_threshold, "likelihood_ratio_neg"] = (1-sensitivity)/specificity + df.loc[proba_threshold, "likelihood_ratio_pos"] = sensitivity/(1-specificity) + df.loc[proba_threshold, "tp"] = cm[1][1] + df.loc[proba_threshold, "fp"] = fp + df.loc[proba_threshold, "tn"] = tn + df.loc[proba_threshold, "fn"] = cm[1][0] + df.loc[proba_threshold, "total_survived"] = np.sum(cm[0]) + df.loc[proba_threshold, "total_deceased"] = np.sum(cm[1]) + return df +``` +``` +cm = onc_calc_cm( + calibrated_results.y, + calibrated_results.p_calibrated, + range_probas=[.10,.20, .30, .40, .50]) +cm.to_csv('./roc_auc/2021_xgb_nonimputed_calibrated_confusion_matrix.csv') +``` +![xgb_nonimputed_cm.png](./images/xgb_nonimputed_cm.png) + +### 6.3.1.6 Saving data for the fairness assessment +***Steps for running the 5_xgb_fairness_assess_get_data.ipynb script*** + +Get the columns of data required to compute fairness assessment and save the file. +``` +inc_age = age +sex +dialtyp=type of dialysis +race +hispanic +``` +- Input: +``` +`medexpressesrd` table from Postgres +``` +- Output: +``` +complete_nonimputed.pickle +``` + +Step 1. Import the libraries +``` +import psycopg2 +import sqlalchemy +from sqlalchemy import create_engine + +import numpy as np +import pandas as pd +import sys +import pickle +``` +Step 2. Connect to the Postgres database + +The credentials required to connect to the database should be inserted in the following snippet of code below: +``` +con = create_engine('postgresql://username:password@location/dbname') +``` +Step 3. Import the columns required for the fairness assessment from the database +``` +df = pd.read_sql_query('''SELECT usrds_id, died_in_90, inc_age, sex, dialtyp, race, hispanic, subset FROM medxpreesrd;''', con) +``` +Step 4. Save the file +``` +with open('complete_fairness_data.pickle', 'wb') as picklefile: + pickle.dump(df, picklefile) +``` + +### 6.3.1.7 Fairness assessment +ML models can perform differently for different categories of patients, so the non-imputed XGBoost model was assessed for fairness, or how well the model performs for each category of interest (demographics—sex, race, and age—as well as initial dialysis modality). Age were binned into the following categories based on clinician input and an example in literature: 18-25, 26-35, 36-45, 46-55, 56-65, 66-75, 76-85, 86+. The USRDS predefined categories for race, sex, and dialysis modality were used for the fairness assessment. + +***Steps for running the 6_xgb_nonimputed_fairness.ipynb script*** + +Calculations for specific groups of patients to assess the fairness of the final model for all patients in the test subsets. Note: Fairness assessment is run on the non-calibrated model results. + +- Input: +``` +complete_nonimputed.pickle +2021_xgb_nonimputed_y_proba.csv +``` +- Output: +``` +2021_xgb_nonimputed_fairness.csv +``` + +Step 1. Import libraries +``` +import numpy as np +import pandas as pd +import pickle + +import datetime +dte = datetime.datetime.now() +dte = dte.strftime("%Y%m%d") + +# import custom functions +import sys +#path to the functions directory +sys.path.append('../../onc_functions/') + + +from fairness import get_fairness_assessment +``` +Step 2. Write the function that calculates AUC and the confusion matrix from the model prediction scores. This function is located and imported from the /onc_functions/fairness.py file. + +``` +def get_fairness_assessment(df, y_proba_col_name, y_true_col_name): + + #turn the continuous age variable into age categories + df['agegroup'] = pd.cut(df.inc_age, + bins=[17, 25, 35, 45, 55, 65, 75, 85, 90], + labels=[1, 2, 3, 4, 5, 6, 7, 8]) + + df = df.drop(columns=['inc_age']) + + #replace NaNs with a large number that does not appear in the data, effectively creating another category for missing values + df.loc[:,['race','dialtyp','hispanic']] = df.loc[:,['race','dialtyp','hispanic']].fillna(100.0, axis=1).copy() + + #Identify the cols for the fairness assessment + fairness_cols = ['agegroup', 'sex','dialtyp', 'race','hispanic'] + + #loop through all categories and values to get counts, auc, and confusion matrix + rows_list = [] + for col in fairness_cols: + for name, c in df.groupby(col): + fairness_dict = {} + fairness_dict['Feature'] = col + fairness_dict['Value'] = name + fairness_dict['Count'] = c.shape[0] + + fairness_dict['AUC'] = roc_auc_score(c[y_true_col_name], c[y_proba_col_name]) + tn, fp, fn, tp = confusion_matrix(y_true = c[y_true_col_name], + y_pred = np.where(c[y_proba_col_name] >= 0.5, 1, 0)).ravel() + fairness_dict['TN'] = tn + fairness_dict['FP'] = fp + fairness_dict['FN'] = fn + fairness_dict['TP'] = tp + rows_list.append(fairness_dict) + + #convert results from a list to a dataframe + df_fairness = pd.DataFrame(rows_list) + return df_fairness +``` +Step 3. Load results from the model and fairness details +``` +pred_df = pd.read_csv('./roc_auc/2021_xgb_nonimputed_y_proba.csv') + +with open('../complete_fairness_data.pickle', 'rb') as f: + dataset = pickle.load(f) + +# merge model results with fairness details +data = pred_df.merge(dataset, how='left', on=['usrds_id']) +``` +Step 4. Calculate fairness assessment +``` +fairness = get_fairness_assessment(data, + y_proba_col_name='score', + y_true_col_name='died_in_90') +``` +Step 5. Save results +``` +fairness.to_csv('./roc_results/' + str(dte) + '_xgb_nonimputed_fairness.csv') +``` +![xgb_nonimputed_fairness.png](./images/xgb_nonimputed_fairness.png) + +***Points to consider*** + +Performing the fairness assessment on the categories of interest gives additional insight into how the model performs by different patient categories of interest (by demographics, etc.). Future researchers should perform fairness assessments to better evaluate model performance, especially for models that may be deployed in a clinical setting. Other methods of assessing fairness include evaluating true positives, sensitivity, positive predictive value, etc. at various threshold across the different groups of interest, which would allow selection of a threshold that balances model performance across the groups of interest. + +### 6.3.1.8 Risk assessment + +***Steps for running the 7_xgb_nonimputed_risk_categories.ipynb script*** + +Note: Risk assessment is run on the non-calibrated model results + +- Input: +``` +complete_fairness_data.pickle +2021_xgb_nonimputed_y_proba.csv +``` +- Output: +``` +2021_xgb_nonimputed_risk_cat.csv +``` +Step 1. Import libraries +``` +import numpy as np +import pandas as pd +import pickle + +import sys +#path to the functions directory +sys.path.append('../../onc_functions/') + +from risk import get_risk_categories + +print('python-' + sys.version) +import datetime +dte = datetime.datetime.now() +dte = dte.strftime("%Y%m%d") +``` +Step 2. Import the details from the fairness assessment +``` +with open('../complete_fairness_data.pickle', 'rb') as f: + dataset = pickle.load(f) +``` + +Step 3. Import the pooled results from the model +``` +pred_df = pd.read_csv('./roc_auc/2021_xgb_nonimputed_y_proba.csv') +``` +Step 4. Merge the details with the results +``` +data = pred_df.merge(dataset, on=['usrds_id']) +``` + +Step 5. Calculate risk. The function **get_risk_categories** is imported from the /onc_functions/risk.py file. + +``` +def get_risk_categories(dataset, y_proba_col_name, y_true_col_name): + + test_x_pd = dataset[dataset.subset > 6].copy().sort_values(by = 'usrds_id') + del dataset + + df = test_x_pd.loc[:,[y_true_col_name,y_proba_col_name]] + + #construct the risk categories from the predicted score + df['risk_categories'] = pd.cut(df[y_proba_col_name], + bins=[-0.1, 0.09, 0.19, 0.29, 0.39, 0.49, 0.59, 0.69, 0.79, 0.89, 0.99], + labels=['0-0.09', '0.1-0.19', '0.2-0.29', '0.3-0.39', '0.4-0.49', + '0.5-0.59','0.6-0.69','0.7-0.79','0.8-0.89','0.9-0.99']) + + #loop through all the categories to get the predicted score + risk_list = [] + for name, c in df.groupby('risk_categories'): + risk_dict = {} + risk_dict['Risk Category'] = name + risk_dict['Count'] = c[y_true_col_name].shape[0] + risk_dict['Count Died in 90'] = c[y_true_col_name].sum() + risk_dict['Count Survived'] = c[y_true_col_name].shape[0]-c[y_true_col_name].sum() + risk_dict['Percent Died in 90'] = c[y_true_col_name].sum()/c[y_true_col_name].shape[0] + + risk_list.append(risk_dict) + + df_risk = pd.DataFrame(risk_list) + return df_risk +``` +Run the function above +``` +risk_cat = get_risk_categories(data, + y_proba_col_name='score', + y_true_col_name='died_in_90') +``` +Step 6. Save +``` +risk_cat.to_csv('./results/' + str(dte) + '_xgb_nonimputed_risk_cat.csv') +``` +![xgb_nonimputed_risk.png](./images/xgb_nonimputed_risk.png) + +## 6.3.2 Imputed XGBoost Model + +All results for the imputed XGBoost model are located in the /roc_results/ directory. + +**Environment** + +The environment used for the Imputed XGBoost model was purchased on Amazon Web Services (AWS): +``` +Name: m5.12xlarge +vCPU: 48 +GPU: 0 +Cores: 24 +Threads per core: 2 +Architecture: x86_64 +Memory: 192 GB +Operating System: Linux (Ubuntu 20.04 Focal Fossa) +Network Performance: 10 GB +Zone: US govcloud west +``` +***Points to consider*** + +Hyperparameter tuning for the imputed XGBoost model required an instance with more than 65 GB of memory. When purchasing more memory we decided to upgrade the number of cores which helped to improve the computation time. The model utilizes parallel processing which used all available cores/CPUs. It took approximately 5 days to run the entire code for the imputed XGBoost model. +### 6.3.2.1 Pre-processing the training dataset + +The data pre-processing for the imputed XGBoost model is similar to the steps for the non-imputed XGBoost model--all categorical variables were one-hot encoded into dummy variables that are binary indicators of each factor in the categorical features (e.g., the sex feature will be turned into 3 columns: sex_1 (male), sex_2 (female), sex_3 (unknown)). An additional step is required to read in the 5 imputed datasets `micecomplete_pmm` and merge with the `medxpreesrd` data. + +***Steps for running the 0_xgb_imputed_preprocess.R script*** + +1. Load `medexpressesrd` table from Postgres and imputed data `micecomplete_pmm`. +2. Merge to create our 5 datasets. Left join `medexpreesrd` and the first set of imputations, keeping imputed cols from imp1, not `medxpreesrd`. +3. Categorical features get one-hot encoded. + +- Input: `medexpressesrd` and `micecomplete_pmm` tables from Postgres +- Output: universe.RData (data ready for modeling) + +Step 1. Load the libraries +``` +library(RPostgres) #Interface to PostgreSQL +library(DBI) #R database interface +library(dplyr) +library(tidyr) +library(skimr) #Summarizing databases +library(data.table) +library(mltools) #data.table and mltools are needed for the "one_hot" function +library(readr) #read rds +``` +Step 2. Load the list of categorical variables +``` +source(file.path("~","ONC_xgboost","category_variables.R")) +``` +Step 3. Connect to the Postgres database and load the `medexpressesrd` table as the variable `universe`. +The credentials required to connect to the database should be inserted in the following snippet of code below: +``` +con <- dbConnect( + RPostgres::Postgres(), + dbname = '', + host = '', + port = '', + user = '', + password = '') +``` +The data from the database is loaded into R as the variable `universe`. +``` +universe=dbGetQuery( + con, + "SELECT * + FROM medxpreesrd") +``` +Step 4. Load the `micecomplete_pmm` table as the variable `imputations_pmm` +``` +imputations_pmm = dbGetQuery( + con, + " + SELECT *, row_number() OVER(PARTITION BY usrds_id) AS impnum + FROM micecomplete_pmm + ") +``` +Step 5. Left join the `medxpreesrd` and `imputations_pmm` tables and keep only the imputed columns from `imputations_pmm` +``` +universe = left_join( + medxpreesrd %>% + select(-c("height", "weight", "bmi", "sercr", "album", "gfr_epi", "heglb", "cdtype")), + imputations_pmm, + by = c("usrds_id", "subset") +) +``` + +Step 6. Set numeric features as numeric type +``` +num_vars = setdiff(names(universe) , categoryVars) + +continuous_vars = c("height", "weight", "bmi", "sercr", "album", "gfr_epi", "heglb") + +num_vars = setdiff(num_vars, continuous_vars) +for (cc in num_vars) { + universe[,cc]=as.numeric(universe[,cc]) +} +``` +Step 7. Separate categorical features from continuous and numeric to one-hot encode +``` +for (c in categoryVars) { + universe[,c]=as.factor(universe[,c]) +} +universe=data.table(universe) +universe=one_hot(as.data.table(universe), naCols=TRUE, dropUnusedLevels = TRUE) +``` +Step 6. Save the pre-processed data +``` +save(universe, file="universe.RData") +``` +***Points to consider*** + +One-hot encoding the categorical variables is preferable to numeric encoding (casting categorical encodings as numeric) as it is a better numeric representation of ordinal variables. However, one-hot encoding increases the number of variables in the training dataset which increases run time and features with more than 5 categories are typically not one-hot encoded for this reason. + +### 6.3.2.2 Hyperparameter tuning for each imputed dataset + +This section tunes the hyperparameters using Bayesian optimization and 5-fold cross validation on each imputed dataset and returns the optimal hyperparameters for each imputed dataset. + +***Steps for running the 1_xgb_imputed_get_hyperparams.R script*** + +This file will run 100 Bayesian models that will: +1. Result in a new range of hyperparameters. +2. Set the dependent variable to died_in_90. +3. Drop non-feature and dependent variable cols ("usrds_id", "subset","died_in_90") +4. Create the train and test sets based on the following "subset" values +``` +trainsubsets = c(0,1,2,3,4,5,6) +``` +- Input: +``` +universe.RData +``` +- Output: +``` +[date]_xgb_results_imputed_1.RData +``` +Step 1. Load the libraries +``` +library(xgboost) +library(dplyr) +library(tidyr) +library(magrittr) +library(smoof) +library(mlrMBO) # for bayesian optimisation +library(skimr) # for summarising databases +library(purrr) # to evaluate the loglikelihood of each parameter set in the random grid search +library(DiceKriging) +library(rgenoud) +library(data.table) +library(mltools) #data.table and mltools are needed for "one_hot" function +library(readr) #read rds +library(rBayesianOptimization) +library(Matrix) +``` +Step 2. Load the one hot encoded data and keep only the training subsets (1-6). +``` +load('universe.RData') + +depvar = "died_in_90" +trainsubsets = c(0,1,2,3,4,5,6) +``` +Step 3. Initiate a list to hold the hyperparameter tuning results from each of the 5 imputed datasets +``` +model_results <-list() +``` + +Step 4. Loop through each imputation +``` +for(i in 1:5){ +---- +} +``` +For each imputation perform the following steps: + +Step 5. Set the training dataset +``` + train=universe %>% + filter(subset %in% trainsubsets, impnum == i) %>% as.data.frame() +``` + +Step 6. Generate the list of indices for 5-fold cross validation +``` +cv_folds = rBayesianOptimization::KFold(train[, depvar], # creating 5 fold validation + nfolds= 5, + stratified = TRUE, + seed = 0) +``` +Step 7. Set the training dataset as numeric and prepare the training dataset as a matrix +``` +train[] <- lapply(train, as.numeric) #force to numeric columns + +options(na.action='na.pass') +trainm <- sparse.model.matrix(died_in_90 ~ ., data = train[, rhscols]) +dtrain <- xgb.DMatrix(data = trainm, label=train[, depvar]) +``` +Step 8. Define the parameters for hyperparameter tuning + +The hyperparameters that were selected for tuning and the ranges that were tuned were: +|Parameter |Range | +|-------------------|---------| +|Eta | 0.001 - 0.8 | +|Gamma | 0 - 9 | +|Lambda | 1 -9 | +|Alpha | 0 - 9 | +|Max Depth | 2 - 10 | +|Minimum Child Weight | 1 - 5 | +|Number of Rounds | 10 - 500 | +|Subsample | 0.2 - 1 | +|Column Sample by Tree | 0.3 - 1 | +|Maxiumum Bin | 255 - 1023 | + +Additional hyperparameters can be found at https://xgboost.readthedocs.io/en/latest/. + +Parameters that were set include: +- Scale_pos_weight as 3.5, which is the square root of the ratio of the negative class (survived the first 90 days of dialysis) and the positive class (died in the first 90 days of dialysis). This parameter handles the class imbalance by weighting the minority class (died in the first 90 days of dialysis). +- Number of iterations as 100. Bayesian optimization will run through 100 iterations to identify the optimal hyperparameters. +- Early stopping rounds to 15, as evaluated using the highest ROC AUC. This parameter ends model training if the ROC AUC has not increased in 15 iterations. + +``` +# Tune parameters --------------------------------------------------- +obj.fun <- smoof::makeSingleObjectiveFunction( + name = "xgb_cv_bayes", + fn = function(x){ + set.seed(12345) + cv <- xgb.cv(params = list( + booster = "gbtree", + scale_pos_weight = sqrt(12), + eta = x["eta"], + max_depth = x["max_depth"], + min_child_weight = x["min_child_weight"], + gamma = x["gamma"], + lambda = x["lambda"], + alpha = x["alpha"], + subsample = x["subsample"], + colsample_bytree = x["colsample_bytree"], + max_bin = x["max_bin"], + objective = 'binary:logistic', + eval_metric = "auc", + tree_method = "hist"), + data=dtrain, + nrounds = x["nround"], + folds = cv_folds, + prediction = FALSE, + showsd = TRUE, + early_stopping_rounds = 15, + verbose = 1) + + cv$evaluation_log[, max(test_auc_mean)] + }, + par.set = makeParamSet( + makeNumericParam("eta", lower = 0.001, upper = 0.8), + makeNumericParam("gamma", lower = 0, upper = 9), + makeNumericParam("lambda", lower = 1, upper = 9), + makeNumericParam("alpha", lower = 0, upper = 9), + makeIntegerParam("max_depth", lower = 2, upper = 10), + makeIntegerParam("min_child_weight", lower = 1, upper = 5), + makeIntegerParam("nround", lower = 10, upper = 500), + makeNumericParam("subsample", lower = 0.2, upper = 1), + makeNumericParam("colsample_bytree", lower = 0.3, upper = 1), + makeIntegerParam("max_bin", lower = 255, upper = 1023) + ), + minimize = FALSE +) + +des = generateDesign(n=length(getParamSet(obj.fun)$pars)+1, # the number of experiments cannot equal the number of variables therefore to increase computation time, we are adding 1 to the total number of hyperparameters. + par.set = getParamSet(obj.fun), + fun = lhs::randomLHS) ## . If no design is given by the user, mlrMBO will generate a maximin Latin Hypercube Design of size 4 times the number of the black-box function's parameters. + +control = makeMBOControl() +control = setMBOControlTermination(control, iters = 100) # number of Bayesian iterations +``` +Step 8. Tune the hyperparameters with Bayesian optimization and 5-fold cross-validation on the training data +``` +results = mbo(fun = obj.fun, + design = des, + control = control, + show.info = TRUE) +``` +Step 9. Save the results to the list `model_results` +``` +model_results[[i]] <- results +``` +This is the end of the loop. To return the best hyperparameters for model i, use the following line of code +``` +model_results[[i]]$x +``` + +Step 10. Save the output to a file +``` +save(model_results, file = "2021_xgb_results_imputed_1.RData") +``` + +***Points to consider*** + +Bayesian optimization was used to narrow down the hyperparameter ranges to consider for a pooled approach to hyperparameter tuning. Using Bayesian optimization to limit the hyperparameter space reduced the time required to run a pooled approach for hyperparameter tuning for the imputed dataset. + +### 6.3.2.3 Pooled Hyperparameter Tuning + +This script runs a random cross validated search on this new range using a pooled approach for all 5 imputed datasets and takes the single set of hyperparameters from the best AUC and run it with the validation data. +The "best" single combination of hyperparameters resulting from this script was fed into 5 individual models for each imputed dataset resulting in 5 predictions averaged from each imputed dataset that was used to compute AUC. + +***Steps for running the 2_xgb_imputed_gridsearch_cv.R script*** + +- Input: +``` +universe.RData +``` +- Output: +``` +2021_pooling_sample.RData +2021_final_hp_results_random_grid_imputed.xlsx +2021_final_hp_results_random_grid_imputed.RData +``` +Step 1. Load the libraries +``` +library(pROC) +library(rsample) +library(xgboost) +library(sqldf) +library(dplyr) +library(tidyr) +library(magrittr) +library(smoof) +library(mlrMBO) # for bayesian optimisation +library(skimr) # for summarising databases +library(purrr) # to evaluate the loglikelihood of each parameter set in the random grid search +library(DiceKriging) +library(rgenoud) +library(data.table) +library(mltools) #data.table and mltools are needed for "one_hot" function +library(readr) #read rds +library(rBayesianOptimization) +library(openxlsx) +library(Matrix) +``` +Step 2. Load the one hot encoded data and keep only the training subsets (1-6). +``` +load("~/universe.RData") +depvar = "died_in_90" +rhscols = setdiff(names(universe), c("usrds_id", "subset", "cdtype")) + +trainsubsets = c(0,1,2,3,4,5,6) +``` +Step 3. Set the seed and the hyperparameter grid from the narrowed ranges from Step 2 + +Set the seed. + +```set.seed(123)``` + +25 iterations will be run for the pooled hyperparameter tuning approach. + +```how_many_models <- 25``` + +Set the updated ranges for each hyperparameter based on the results from Bayesian optimization and randonly generate 25 values for each hyperparameter. +``` +eta <- data.frame(eta = runif(how_many_models,min = 0.04852942, max = 0.08619335)) +gamma <- data.frame(gamma = runif(how_many_models,min = 0.766442, max = 6.013658)) +lambda <- data.frame(lambda = runif(how_many_models,min = 5.845102, max = 8.751962)) +alpha <- data.frame(alpha = runif(how_many_models,min = 6.516213, max = 8.719468)) +max_depth <- data.frame(max_depth = sample(6:7, how_many_models, replace=TRUE)) +min_child_weight <- data.frame(min_child_weight = sample(1:4, how_many_models, replace=TRUE)) +nround <- data.frame(nround = sample(419:499, how_many_models, replace=TRUE)) +subsample <- data.frame(subsample = runif(how_many_models,min = 0.7314413, max = 0.8471972)) +colsample_bytree <- data.frame(colsample_bytree = runif(how_many_models,min = 0.5921707, max = 0.8566342)) +max_bin <- data.frame(max_bin = sample(529:972, how_many_models, replace=TRUE)) +``` +Initiate the hyperparameter grid. +``` +random_grid <-eta %>% + bind_cols(gamma) %>% + bind_cols(lambda) %>% + bind_cols(alpha) %>% + bind_cols(max_depth) %>% + bind_cols(min_child_weight) %>% + bind_cols(nround) %>% + bind_cols(subsample) %>% + bind_cols(colsample_bytree) %>% + bind_cols(max_bin) %>%as_tibble() + + +df.params <- bind_rows(random_grid) %>% + mutate(rownum = row_number(), + model = row_number()) +list_of_param_sets <- df.params %>% nest(-rownum) + +colnames(list_of_param_sets) <- c("model","hyperparamters") +``` +Step 4. Prepare the training dataset +```train_full = universe %>% + filter(subset <=6 ) %>% as.data.frame() +``` +Step 5. Loop through each imputation +``` +for(i in 1:5){ +---- +} +``` +For each imputation perform the following steps: + +Step 6. Set the training dataset +``` +train_onc=train_full %>% + filter(impnum == i) %>% as.data.frame() +``` +Step 7. Sort the training dataset by `usrds_id` +``` +train_onc = train_onc[order(train_onc$usrds_id),] #We sort the data to make sure an usrsd_id will always end up in the training or validation regardless of which imputed dataset we are using +``` +Step 8. Remove columns containing all NAs +``` +all_na <- function(x) any(!is.na(x)) #creating function that removes columns containing all NAs +train_onc <- train_onc %>% select_if(all_na) #removing the columns containing all NAs +``` +Step 9. Set all columns to numeric and set the seed +``` +train_onc[] <- lapply(train_onc, as.numeric) #force to numeric columns + +set.seed(2369) +``` +Step 10. Set the train/test split (70% for training and 30% for validation) +``` +tr_te_split <- rsample::initial_split(train_onc, prop = 7/10) #70% for training, 30% for validation/test +train_onc <- rsample::training(tr_te_split) %>% as.data.frame() +test_onc <- rsample::testing(tr_te_split) %>% as.data.frame() +``` +Step 11. Prepare training and validation/test set for modeling +``` +options(na.action='na.pass') +trainm <- sparse.model.matrix(died_in_90 ~ ., data = train_onc[, c(rhscols,"died_in_90")]) +dtrain <- xgb.DMatrix(data = trainm, label=train_onc[, depvar]) + +testm <- sparse.model.matrix(died_in_90 ~ ., data = test_onc[, c(rhscols,"died_in_90")]) +dtest <- xgb.DMatrix(data = testm, label=test_onc[, depvar]) +watchlist <- list(train = dtrain, eval = dtest) +``` +Step 12. Write function to loop through all 25 hyperparameter combinations and run the model +``` +random_grid_results <- list_of_param_sets %>% + mutate(results = map(hyperparamters, function(x){ + + message(paste0("model #", x$model, + " eta = ", x$eta, + " max.depth = ", x$max_depth, + " min_child_weigth = ", x$min_child_weight, + " subsample = ", x$subsample, + " colsample_bytree = ", x$colsample_bytree, + " gamma = ", x$gamma, + " nrounds = ", x$nround)) + + set.seed(12345) + singleModel <- xgb.train(params = list( + booster = "gbtree", + scale_pos_weight = sqrt(12), + eta = x$eta, + max_depth = x$max_depth, + min_child_weight = x$min_child_weight, + gamma = x$gamma, + lambda = x$lambda, + alpha = x$alpha, + subsample = x$subsample, + colsample_bytree = x$colsample_bytree, + max_bin = x$max_bin, + objective = 'binary:logistic', + eval_metric = "auc"), + data=dtrain, + nrounds = x$nround, + prediction = FALSE, + watchlist = watchlist, + showsd = TRUE, + early_stopping_rounds = 15, + verbose = 2) + + output <- list(score = predict(singleModel, dtest), + id = test_onc$usrds_id + ) + return(output) + })) +``` +Step 12: Add all results to a list +```all[[i]] <- random_grid_results``` +This is the end of the loop. + +Step 13: Loop through each set of the 25 hyperparameters to pool the 5 prediction scores from each imputation together and calculate AUC +``` +for(i in 1:how_many_models){ + + one <- as.data.frame(data.table::transpose(all[[1]]$results[[i]]))[1,] %>% + tidyr::gather(key = "usrds_id", value = "score") %>% + mutate(usrds_id = all[[1]]$results[[i]]$id) + + two <- as.data.frame(data.table::transpose(all[[2]]$results[[i]]))[1,] %>% + tidyr::gather(key = "usrds_id", value = "score") %>% + mutate(usrds_id = all[[2]]$results[[i]]$id) + + third <- as.data.frame(data.table::transpose(all[[3]]$results[[i]]))[1,] %>% + tidyr::gather(key = "usrds_id", value = "score") %>% + mutate(usrds_id = all[[3]]$results[[i]]$id) + + fourth <- as.data.frame(data.table::transpose(all[[4]]$results[[i]]))[1,] %>% + tidyr::gather(key = "usrds_id", value = "score") %>% + mutate(usrds_id = all[[4]]$results[[i]]$id) + + fifth <- as.data.frame(data.table::transpose(all[[5]]$results[[i]]))[1,] %>% + tidyr::gather(key = "usrds_id", value = "score") %>% + mutate(usrds_id = all[[5]]$results[[i]]$id) + + pooling = one %>% + inner_join(two, by = "usrds_id") %>% + inner_join(third, by = "usrds_id") %>% + inner_join(fourth, by = "usrds_id") %>% + inner_join(fifth, by = "usrds_id") + + pooling$averaged <- apply(pooling[2:ncol(pooling)], 1, mean) #averaging scores + + pooling <- left_join(pooling, test_onc %>% select("usrds_id","died_in_90"), by = "usrds_id") + + #calculate AUC + auc <- pROC::auc(pooling$died_in_90, pooling$averaged) #compute AUC + } + # Add the results to a dataframe + toAdd <- data.frame(hyper = all[[1]]$hyperparamters[[i]], + auc = auc) + + final_hp_results <- rbind(final_hp_results,toAdd) + ``` +Step 14. Save the output file +``` +save(final_hp_results, file = "2021_final_hp_results_random_grid_imputed.RData") + +openxlsx::write.xlsx(as.data.frame(final_hp_results), file = "2021_final_hp_results_random_grid_imputed.xlsx", + sheetName='Sheet1', row.names=FALSE,showNA = F) +``` + +***Points to consider*** + +Using a random grid search on the narrowed hyperparameter ranges allowed for the pooling of the model prediction scores from each imputed dataset on each hyperparameter combination to produce one AUC, which resulted in a much shorter compute time to identify the optimal hyperparameters. + +### 6.3.2.4 Final Imputed XGBoost Model + +***Steps for running the 3_xgb_imputed_final_hyperparams.R script*** + +Run final model using the best combination of hyperparameters from the previous step on each of the 5 imputed datasets. Pool results by averaging across samples to get the the ROC AUC, confusion matrix and feature importance +- Input: +``` +universe.RData +``` + - Output: + ``` + 2021_final_hp_results_single_imputed_xgb.xlsx + 2021_final_hp_results_single_imputed_xgb.RData + 2021_conf_matrix.RData + 2021_myplot_xgb.RData + 2021_Rplots.pdf + 2021_all_features.RData + 2021_averaged_feature_importance_xgb.RData + 2021_xgb_pooling_results_final_roc.csv + ``` +Step 1. Load the libraries +``` +library(pROC) +library(rsample) +library(RPostgres) +library(DBI) +library(xgboost) +library(sqldf) +library(dplyr) +library(tidyr) +library(magrittr) +library(smoof) +library(mlrMBO) # for bayesian optimisation +library(skimr) # for summarising databases +library(purrr) # to evaluate the loglikelihood of each parameter set in the random grid search +library(DiceKriging) +library(rgenoud) +library(data.table) +library(mltools) #data.table and mltools are needed for "one_hot" function +library(readr) #read rds +library(rBayesianOptimization) +library(openxlsx) +library(Matrix) +library(stringr) +``` +Step 2. Load the data and set the seed +``` +load('~/universe.RData') +depvar = "died_in_90" + +rhscols = setdiff(names(universe), c("usrds_id", "subset", "died_in_90")) + +trainsubsets = c(0,1,2,3,4,5,6) +testsubsets = c(7,8,9) + +set.seed(123) +``` +Step 3. Set the number of models (1) and set the optimal hyperparameters from the previous section +``` +how_many_models <- 1 +eta <- data.frame(eta = 0.0501135) +gamma <- data.frame(gamma = 2.937342) +lambda <- data.frame(lambda = 8.20660) +alpha <- data.frame(alpha = 7.27306) +max_depth <- data.frame(max_depth = 7) +min_child_weight <- data.frame(min_child_weight = 2) +nround <- data.frame(nround = 493) +subsample <- data.frame(subsample = 0.7513711) +colsample_bytree <- data.frame(colsample_bytree = 0.6611578) +max_bin <- data.frame(max_bin = 935) +``` +Step 4. Initiate the hyperparameter grid +``` +random_grid <-eta %>% + bind_cols(gamma) %>% + bind_cols(lambda) %>% + bind_cols(alpha) %>% + bind_cols(max_depth) %>% + bind_cols(min_child_weight) %>% + bind_cols(nround) %>% + bind_cols(subsample) %>% + bind_cols(colsample_bytree) %>% + bind_cols(max_bin) %>%as_tibble() + + +df.params <- bind_rows(random_grid) %>% + mutate(rownum = row_number(), + model = row_number()) +list_of_param_sets <- df.params %>% nest(-rownum) + +colnames(list_of_param_sets) <- c("model","hyperparamters") +``` +Step 5. Set the training and test datasets +``` +train_full = universe %>% + filter(subset <=6 ) %>% as.data.frame() + +test_full = universe %>% + filter(subset > 6 ) %>% as.data.frame() +``` + +Step 6. Loop through each imputation +``` +for(i in 1:5){ +---- +} +``` +For each imputation perform the following steps: + +Step 7. Prepare the training and the test data for modeling +``` +train_onc=train_full %>% + filter(impnum == i) %>% as.data.frame() + +train_onc = train_onc[order(train_onc$usrds_id),] + +rownames(train_onc) <- train_onc$usrds_id #preserving usrds_id as rownames because usrds_id will be removed in next line + +train_onc <- train_onc[, c(rhscols,"died_in_90")] #selecting variables + +all_na <- function(x) any(!is.na(x)) #creating function that removes columns containing all NAs +train_onc <- train_onc %>% select_if(all_na) #removing the columns containing all NAs + +train_onc[] <- lapply(train_onc, as.numeric) #force to numeric columns + +print(paste("dimensions for train_onc:",dim(train_onc))) + +options(na.action='na.pass') +trainm <- sparse.model.matrix(died_in_90 ~ ., data = train_onc) +dtrain <- xgb.DMatrix(data = trainm, label=train_onc[, depvar]) +rm(trainm) +rm(train_onc) +gc() + +#### test pre-processing + +test_onc=test_full %>% + filter(impnum == i) %>% as.data.frame() + +test_onc = test_onc[order(test_onc$usrds_id),] + +test_ids <- test_onc$usrds_id #preserving usrds_id +rownames(test_onc) <- test_onc$usrds_id #preserving usrds_id as rownames because usrds_id will be removed in next line + +test_onc <- test_onc[, c(rhscols,"died_in_90")] #selecting variables + +test_onc <- test_onc %>% select_if(all_na) #removing the columns containing all NAs + +test_onc[] <- lapply(test_onc, as.numeric) #force to numeric columns + +print(paste("dimensions for test_onc:",dim(test_onc))) + +options(na.action='na.pass') +testm <- sparse.model.matrix(died_in_90 ~ ., data = test_onc) +dtest <- xgb.DMatrix(data = testm, label=test_onc[, depvar]) +rm(testm) +gc() + ``` +Step 8. Write function to run the model + ``` + watchlist <- list(train = dtrain, eval = dtest) + + random_grid_results <- list_of_param_sets %>% + mutate(results = map(hyperparamters, function(x){ + + message(paste0("model #", x$model, + " eta = ", x$eta, + " max.depth = ", x$max_depth, + " min_child_weigth = ", x$min_child_weight, + " subsample = ", x$subsample, + " colsample_bytree = ", x$colsample_bytree, + " gamma = ", x$gamma, + " nrounds = ", x$nround)) + + set.seed(12345) + singleModel <- xgb.train(params = list( + booster = "gbtree", + scale_pos_weight = sqrt(12), + eta = x$eta, + max_depth = x$max_depth, + min_child_weight = x$min_child_weight, + gamma = x$gamma, + lambda = x$lambda, + alpha = x$alpha, + subsample = x$subsample, + colsample_bytree = x$colsample_bytree, + max_bin = x$max_bin, + objective = 'binary:logistic', + eval_metric = "auc"), + data=dtrain, + nrounds = x$nround, + prediction = FALSE, + watchlist = watchlist, + showsd = TRUE, + early_stopping_rounds = 15, + verbose = 0) +``` +Step 9. Obtain feature importance and save prediction scores and USRDS IDs to a list +``` + feature_imp <- xgb.importance(singleModel$feature_names, + model = singleModel) + all_features[[i]] <- feature_imp # add feature_imp to list + + output <- list(score = predict(singleModel, dtest), + id = test_ids +``` +Step 10. Add modeling results to a list +```all[[i]] <- random_grid_results``` +This is the end of the loop. + +Step 11: Pool the 5 prediction scores from each imputation together and calculate AUC +``` +final_hp_results_single <- data.frame() + +for(i in 1:how_many_models){ + + one <- as.data.frame(data.table::transpose(all[[1]]$results[[i]]))[1,] %>% + tidyr::gather(key = "usrds_id", value = "score") %>% + mutate(usrds_id = all[[1]]$results[[i]]$id) + + two <- as.data.frame(data.table::transpose(all[[2]]$results[[i]]))[1,] %>% + tidyr::gather(key = "usrds_id", value = "score") %>% + mutate(usrds_id = all[[2]]$results[[i]]$id) + + third <- as.data.frame(data.table::transpose(all[[3]]$results[[i]]))[1,] %>% + tidyr::gather(key = "usrds_id", value = "score") %>% + mutate(usrds_id = all[[3]]$results[[i]]$id) + + fourth <- as.data.frame(data.table::transpose(all[[4]]$results[[i]]))[1,] %>% + tidyr::gather(key = "usrds_id", value = "score") %>% + mutate(usrds_id = all[[4]]$results[[i]]$id) + + fifth <- as.data.frame(data.table::transpose(all[[5]]$results[[i]]))[1,] %>% + tidyr::gather(key = "usrds_id", value = "score") %>% + mutate(usrds_id = all[[5]]$results[[i]]$id) + + pooling = one %>% + inner_join(two, by = "usrds_id") %>% + inner_join(third, by = "usrds_id") %>% + inner_join(fourth, by = "usrds_id") %>% + inner_join(fifth, by = "usrds_id") + + pooling$averaged <- apply(pooling[2:ncol(pooling)], 1, mean) #averaging scores + pooling$usrds_id <- as.character(pooling$usrds_id) + + test_onc$usrds_id <- as.character(rownames(test_onc)) + + pooling <- left_join(pooling, test_onc %>% select("usrds_id","died_in_90"), by = "usrds_id") + + pooling$predicted <- ifelse(pooling$averaged > 0.5, 1,0) + + print("pooling summary after left_join():") + summary(pooling) + + print("conf matrix:") + table(pooling$predicted, pooling$died_in_90) + conf_matrix <- table(pooling$predicted, pooling$died_in_90) + + save(conf_matrix, file = "2021_conf_matrix.RData") +``` +Step 12. For each imputation, calculate the confusion matrix and model evaluation metrics at a threshold of 0.5 +``` + tp <- conf_matrix[2,2] + fp <- conf_matrix[2,1] + fn <- conf_matrix[1,2] + tn <- conf_matrix[1,1] + + sensitivity = tp / (tp + fn) + specificity = tn / (fp + tn) + fpr = 1 - specificity + tpr = sensitivity + LR = sensitivity / (1 - specificity) + ppv = tp / (tp + fp) + npv = tn / (tn + fn) + f1_score = 2 * ppv * sensitivity / (ppv + sensitivity) + + accuracy <- mean(pooling$predicted == pooling$died_in_90) +``` +Step 13. Plot ROC AUC +``` + myplot <- pROC::plot.roc(pooling$died_in_90, pooling$averaged) + + save(myplot, file = "2021_myplot_xgb.RData") +``` +Step 14. Save final non-imputed XGBoost modeling results +``` + write.csv(pooling, '2021_xgb_pooling_results_final_roc.csv') + + final_hp_results_single <- rbind(final_hp_results_single,toAdd) + + save(final_hp_results_single, file = "2021_final_hp_results_single_imputed_xgb.RData") + openxlsx::write.xlsx(as.data.frame(final_hp_results_single), file = "2021_final_hp_results_single_imputed_xgb.xlsx", + sheetName='Sheet1', row.names=FALSE,showNA = F) +} + +print("saving the feature importance") +save(all_features, file = "2021_all_features.RData") +``` +Step 15: Average feature importance across imputations and save file +``` +#averging the feature importance +averaged <- all_features %>% reduce(inner_join, by = "Feature") %>% as.data.frame() + +rownames(averaged) <- averaged$Feature +averaged = averaged %>% select(contains("Gain")) + +averaged$average = as.data.frame(apply(averaged, 1, mean)) #compute average + +averaged$feature = rownames(averaged) + +save(averaged, file = "2021_averaged_feature_importance_xgb.RData") +``` + +### 6.3.2.5 Calibration +The calibration curve shows the reliability of the model by each prediction score category, the number of patients that fall into each category, and the proportion of patients in each category who actually died in the first 90 days following dialysis initiation. + +***Steps to running the 4_xgb_imputed_calibration.ipynb script*** +- Input: +``` +2021_xgb_pooling_results_final_roc.csv +``` +- Output: +``` +model_calibrated_xgb_imputed.pickle +y_calibrated_xgb_imputed.pickle +``` + +Step 1. Import libraries +``` +import pandas as pd +import numpy as np +import pickle + +import sys +#path to the functions directory +sys.path.append('../../onc_functions/') + +# import custom functions +from plot_functions import onc_plot_calibration_curve +from calibrate_onc import calibrate_onc + +#connect to posgres database +import psycopg2 +import sqlalchemy +from sqlalchemy import create_engine + +con = create_engine('postgresql://username:password@location/dbname') +``` +Step 2. Load results from the XGBoost Imputed model +``` +# load results from the aucroc evaluated model) +pred_df = pd.read_csv('./roc_results/2021_xgb_pooling_results_final_roc.csv') +pred_df = pred_df.loc[:,['averaged','died_in_90','usrds_id']] +``` +Step 3. Plot the original model's calibration curve. This function **onc_plot_calibration_curve** is located in the /onc_functions/plot_functions.py file. +``` +def onc_plot_calibration_curve(y_true, y_proba, label, filename): + + #calculate numbers to plot + clf_score = brier_score_loss(y_true, y_proba, pos_label=1) + fraction_of_positives, mean_predicted_value = \ + calibration_curve(y_true, y_proba, n_bins=10) + # set up plot + fig1 = plt.figure(1, figsize=(10,10))#,dpi=400) + ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2) + ax2 = plt.subplot2grid((3, 1), (2, 0)) + + #plot the reference for a prefectly calibrated model + ax1.plot([0, 1], [0, 1], "k:", label="Reference Line") + + # plot the calibration curve + ax1.plot(mean_predicted_value, fraction_of_positives, "ks-", + label=label) + + # plot histogram of predicted values + ax2.hist(y_proba, range=(0, 1), bins=10, label=label, + histtype="step", lw=2) + + # set axes and other figure parameters + ax1.set_ylabel("Observed Event Rate") + ax1.set_xlabel("Predicted Event Rate") + ax1.set_ylim([-0.05, 1.05]) + ax1.legend(loc="lower right") + + ax2.set_xlabel("Mean predicted value") + ax2.set_ylabel("Count") + ax2.legend(loc="upper right", ncol=1) + + plt.rc('axes', labelsize=22) # fontsize of the x and y labels + plt.rc('xtick', labelsize=15) # fontsize of the tick labels + plt.rc('ytick', labelsize=15) # fontsize of the tick labels + plt.rc('legend', fontsize=20) # legend fontsize + #save figure resolution + plt.savefig(filename + ".png", dpi=400, transparent=True) + plt.show() +``` +Run the function above. +``` +onc_plot_calibration_curve( + y_true=pred_df.died_in_90, + y_proba=pred_df.averaged, + label='XGBoost_imputed', + filename='./roc_results/xgb_imputed_orig_calibration') +``` +![xgb_imputed_orig_calibration](./images/xgb_imputed_orig_calibration.png) + +The XGBoost model can be calibrated by training an isotonic regression on a portion of the testing set. (Model calibration is performed as probabilities of death in the first 90 days are more informative and useful for clinicians than a simple binary prediction. In order to produce valid probability estimates, predicted events rates should track observed rates across the full range of predicted risk.) + +Step 4. Load the subset for each ID: +``` +df = pd.read_sql_query(''' +SELECT usrds_id, subset FROM medxpreesrd;''', con) +``` +Merge the subset details with the predictions +``` +data = pd.merge(pred_df, df, how="left", on="usrds_id") +``` +The next steps are inside function **calibrate_onc** located in the /onc_functions/calibrate_onc.py file. + +Step 5. Split the predictions from the test set (how we evaluated the model) into a test/train for the calibration (isotonic regression classifier). Split test data (subsets 7-9) into new train (7-8)/test (9) sets +``` +calibration_train_set = data[((data.subset==7)|(data.subset==8))].copy() +calibration_test_set = data[data.subset==9].copy() +``` +Step 6. Define the calibration model +``` +ir = IsotonicRegression(out_of_bounds="clip") +``` +Step 7. Fit the model to the XGBoost predictions from the (new) training set +``` +ir.fit(calibration_train_set.score, calibration_train_set.y ) +``` +Step 8. Evaluate the model using the (new) test set +``` +p_calibrated = ir.transform(calibration_test_set.score) +calibration_test_set['p_calibrated'] = p_calibrated +``` +Step 9. Save +``` +with open(path + 'model_calibrated_' + model_name + '.pickle', 'wb') as picklefile: + pickle.dump(ir,picklefile) + +with open(path + 'y_calibrated_' + model_name + '.pickle', 'wb') as picklefile: + pickle.dump(calibration_test_set, picklefile) +``` +Step 10. Print the scores from the original and calibrated model. The function **print_calibrated_results** is found in the /onc_functions/calibrate_onc.py file. +``` +def print_calibrated_results(y_true, y_pred, y_calibrated): + '''print scores for pre and post calibration''' + + acc = accuracy_score(y_true, np.round(y_pred)) + acc_calibrated = accuracy_score(y_true, np.round(y_calibrated )) + print ("accuracy - original/calibrated:", acc, "/", acc_calibrated) + + auc = roc_auc_score(y_true, y_pred) + auc_calibrated = roc_auc_score(y_true, y_calibrated) + print ("ROC AUC - original/calibrated: ", auc, "/", auc_calibrated) + + pr = average_precision_score(y_true, y_pred) + pr_calibrated = average_precision_score(y_true, y_calibrated ) + print ("avg precision - original/calibrated:", pr, "/", pr_calibrated) + + clf_score = brier_score_loss(y_true, y_calibrated, pos_label=1) + print("\tBrier: %1.3f" % (clf_score)) +``` +Run these 2 calibration functions. +``` +calibrated_results = calibrate_onc(data, path='./roc_results/',model_name='xgb_imputed') +``` +### 6.3.2.6 Plotting calibrated results +***Steps to running the 5_xgb_imputed_calibrated_plots.ipynb script*** + +- Input: +``` +y_calibrated_xgb_imputed.pickle +``` +- Output: +``` +xgb_imputed_calibration.jpeg +xgb_imputed_mortality_bar.jpeg +xgb_imputed_roc_auc_bw.jpeg +2021_xgb_imputed_calibrated_confusion_matrix.csv +``` +Step 1. Import libraries +``` +import pandas as pd +import numpy as np +import pickle + +#import custom plotting functions +from plot_functions import onc_plot_calibration_curve, onc_calc_cm, onc_plot_roc, onc_plot_precision_recall, onc_plot_risk +``` +Step 2. Load results from the calibrated model +``` +with open('./roc_results/y_calibrated_xgb_imputed.pickle', 'rb') as picklefile: + calibrated_results = pickle.load(picklefile) +``` +Step 3. Plot the calibration curve of the calibrated model using the same **onc_plot_calibration_curve** function from /onc_functions/plot_functions.py +``` +onc_plot_calibration_curve( + y_true=calibrated_results.y, + y_proba=calibrated_results.p_calibrated, + label='XGBoost imputed calibrated', + filename='./roc_results/xgb_imputed_calibrated') +``` +![xgb_imputed_calibration.jpeg](./images/xgb_imputed_calibration.jpeg) + +Step 4. Plot the Risk of the calibrated model. This function **onc_plot_risk** is located and imported from plot_functions.py +``` +def onc_plot_risk(y_true, y_proba, label, filename): + # calculate values for plot + fraction_of_positives, mean_predicted_value = \ + calibration_curve(y_true, y_proba, n_bins=10) + + # set up figure params + fig1 = plt.figure(1, figsize=(12,30),dpi=400) + ax1 = plt.subplot2grid((7, 1), (0, 0), rowspan=2) + + # bar plot + xs = np.arange(len(fraction_of_positives)) + ax1.bar(xs, mean_predicted_value, color='k', width = 0.25, label=label) + ax1.bar(xs+.25, fraction_of_positives, color='gray', width = 0.25, label='Observed') + + #more figure settings + plt.xticks(xs, np.arange(1, len(xs)+1, 1)) + ax1.set_ylabel("Mortality Rate") + ax1.set_xlabel("Decile of Predicted Mortality Risk") + ax1.legend(loc="upper left") + plt.rc('axes', labelsize=22) # fontsize of the x and y labels + plt.rc('xtick', labelsize=15) # fontsize of the tick labels + plt.rc('ytick', labelsize=15) # fontsize of the tick labels + plt.rc('legend', fontsize=20) # legend fontsize + #save plot + plt.savefig(filename + ".png", dpi=400, transparent=True) +``` +``` +onc_plot_risk( + y_true=calibrated_results.y, + y_proba=calibrated_results.p_calibrated, + label='Predicted (XGBoost Imputed)', + filename='xgb_imputed_mortality_bar') +``` + +![xgb_imputed_mortality_bar.jpeg](./images/xgb_imputed_mortality_bar.jpeg) + +Step 5. Plot the ROC AUC of the calibrated model. This function **onc_plot_roc** is located and imported from plot_functions.py +``` +def onc_plot_roc(y_true, y_pred, model_name, **kwargs): + ''' + Plot the ROC AUC and return the test ROC AUC results. + INPUT: y_true, y_pred, model_name, **kwargs + ''' + + #calc values for plot + false_positives, true_positives, threshold = roc_curve(y_true, y_pred) + c_roc_auc_score = auc(false_positives, true_positives) + + #set figure params + fig1 = plt.figure(1, figsize=(12,30),dpi=400) + ax1 = plt.subplot2grid((7, 1), (0, 0), rowspan=2) + + #plot reference line for chance + ax1.plot([0, 1], [0, 1], linestyle='--', lw=2, color='gray', + label='Chance', alpha=.8) + + # plot AUC ROC + ax1.plot(false_positives, true_positives, + label=r'ROC (AUC = %0.3f)' % (c_roc_auc_score), + lw=2, alpha=.8, color = 'k') + + # additional figure params + ax1.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],) + ax1.legend(loc="lower right") + plt.xlabel('1-Specificity') + plt.ylabel('Sensitivity') + plt.rc('axes', labelsize=22) # fontsize of the x and y labels + plt.rc('xtick', labelsize=15) # fontsize of the tick labels + plt.rc('ytick', labelsize=15) # fontsize of the tick labels + plt.rc('legend', fontsize=20) # legend fontsize + # save plot + plt.savefig(model_name + "_calibrated_roc_auc_bw.png", dpi=400, transparent=True) + plt.show() +``` +``` +onc_plot_roc( + y_true=calibrated_results.y, + y_pred=calibrated_results.p_calibrated, + model_name='xgb_imputed'); +``` +![xgb_imputed_roc_auc_bw_nothreshold_hires_crop.png](./images/xgb_imputed_roc_auc_bw_nothreshold_hires_crop.png) + +Step 6. Save the performance metrics at multiple thresholds. The following function is imported from /onc_functions/plot_functions.py +``` +def onc_calc_cm(y_true, y_predictions, range_probas=[0.1,0.5]): + ''' + Plot the confusion matrix and scores for multiple thresholds + ''' + df = pd.DataFrame(index = range_probas, + columns=['threshold','sensitivity','specificity', + 'likelihood_ratio_neg','likelihood_ratio_pos', + 'tp','fp','tn','fn','total_survived','total_deceased',]) + for proba_threshold in range_probas: + + cm = confusion_matrix(y_true, y_predictions > proba_threshold) + tn = cm[0][0] + fp = cm[0][1] + + sensitivity = recall_score(y_true, y_predictions > proba_threshold) + specificity = tn / (tn + fp) + + df.loc[proba_threshold, "threshold"] = proba_threshold + df.loc[proba_threshold,"sensitivity"] = sensitivity + df.loc[proba_threshold, "specificity"] = specificity + df.loc[proba_threshold, "likelihood_ratio_neg"] = (1-sensitivity)/specificity + df.loc[proba_threshold, "likelihood_ratio_pos"] = sensitivity/(1-specificity) + df.loc[proba_threshold, "tp"] = cm[1][1] + df.loc[proba_threshold, "fp"] = fp + df.loc[proba_threshold, "tn"] = tn + df.loc[proba_threshold, "fn"] = cm[1][0] + df.loc[proba_threshold, "total_survived"] = np.sum(cm[0]) + df.loc[proba_threshold, "total_deceased"] = np.sum(cm[1]) + return df +``` +``` +cm = onc_calc_cm( + calibrated_results.y, + calibrated_results.p_calibrated, + range_probas=[.10,.20, .30, .40, .50]) +cm.to_csv('./roc_results/2021_xgb_imputed_calibrated_confusion_matrix.csv') +cm +``` +![xgb_imputed_cm.png](./images/xgb_imputed_cm.png) + +### 6.3.2.7 Saving data for the fairness assessment + +***Steps to running the 6_xgb_fairness_assess_get_data.ipynb script*** + +Get the columns of data required to compute fairness assessment and save +``` +inc_age = age +sex +dialtyp=type of dialysis +race +``` +- Input:`medexpressesrd` table from Postgres +- Output: +``` +complete_fairness_data.pickle +``` + +Step 1. Import the libraries +``` +import psycopg2 +import sqlalchemy +from sqlalchemy import create_engine + +import numpy as np +import pandas as pd +import sys +import pickle +``` +Step 2. Connect to the Postgres database + +The credentials required to connect to the database should be inserted in the following snippet of code below: +``` +con = create_engine('postgresql://username:password@location/dbname') +``` +Step 3. Import the columns required for the fairness assessment from the database +``` +df = pd.read_sql_query('''SELECT usrds_id, died_in_90, inc_age, sex, dialtyp, race, hispanic, subset FROM medxpreesrd;''', con) +``` +Step 4. Save the files +``` +with open('complete_fairness_data.pickle', 'wb') as picklefile: + pickle.dump(df, picklefile) +``` +### 6.3.2.8 Fairness assessment + +ML models can perform differently for different categories of patients, so the imputed XGBoost model was assessed for fairness, or how well the model performs for each category of interest (demographics—sex, race, and age—as well as initial dialysis modality). Age is binned into the following categories based on UCSF clinician input and an example in literature: 18-25, 26-35, 36-45, 46-55, 56-65, 66-75, 76-85, 86+. The USRDS predefined categories for race, sex, and dialysis modality were used for the fairness assessment. + +***Steps for running the 7_xgb_imputed_fairness.ipynb script*** + +Calculations for specific groups of patients to assess the fairness of the final model for all patients in the test subsets. For the fairness assessment for the imputed XGBoost model, all results are for imputation #5 for the non-calibrated model. + +- Input: +``` +2021_xgb_pooling_results_final_roc.csv +complete_fairness_data.pickle +``` +- Output: +``` +2021_xgb_imputed_fairness.csv +``` +Step 1. Import libraries +``` +import numpy as np +import pandas as pd + +import pickle +import datetime +import sys +#path to the functions directory +sys.path.append('../../onc_functions/') + +# import custom function +from fairness import get_fairness_assessment +``` +Step 2. Write the function that calculates AUC and the confusion matrix from the model prediction scores. This function is located and imported from the /onc_functions/fairness.py file. + +``` +def get_fairness_assessment(df, y_proba_col_name, y_true_col_name): + + #turn the continuous age variable into age categories + df['agegroup'] = pd.cut(df.inc_age, + bins=[17, 25, 35, 45, 55, 65, 75, 85, 90], + labels=[1, 2, 3, 4, 5, 6, 7, 8]) + + df = df.drop(columns=['inc_age']) + + #replace NaNs with a large number that does not appear in the data, effectively creating another category for missing values + df.loc[:,['race','dialtyp','hispanic']] = df.loc[:,['race','dialtyp','hispanic']].fillna(100.0, axis=1).copy() + + #Identify the cols for the fairness assessment + fairness_cols = ['agegroup', 'sex','dialtyp', 'race','hispanic'] + + #loop through all categories and values to get counts, auc, and confusion matrix + rows_list = [] + for col in fairness_cols: + for name, c in df.groupby(col): + fairness_dict = {} + fairness_dict['Feature'] = col + fairness_dict['Value'] = name + fairness_dict['Count'] = c.shape[0] + + fairness_dict['AUC'] = roc_auc_score(c[y_true_col_name], c[y_proba_col_name]) + tn, fp, fn, tp = confusion_matrix(y_true = c[y_true_col_name], + y_pred = np.where(c[y_proba_col_name] >= 0.5, 1, 0)).ravel() + fairness_dict['TN'] = tn + fairness_dict['FP'] = fp + fairness_dict['FN'] = fn + fairness_dict['TP'] = tp + rows_list.append(fairness_dict) + + #convert results from a list to a dataframe + df_fairness = pd.DataFrame(rows_list) + return df_fairness +``` +Step 3. Load results from the model and fairness details +``` +pred_df = pd.read_csv('./roc_results/2021_xgb_pooling_results_final_roc.csv') + +with open('../complete_fairness_data.pickle', 'rb') as f: + dataset = pickle.load(f) + +# merge model results with fairness details +data = pred_df.merge(dataset, how='left', on=['usrds_id','died_in_90']) +``` +Step 4. Calculate fairness assessment +``` +fairness = get_fairness_assessment(data, + y_proba_col_name='averaged', + y_true_col_name='died_in_90') +``` +Step 5. Save results +``` +fairness.to_csv('./roc_results/' + str(dte) + '_xgb_imputed_fairness.csv') +``` +![xgb_imputed_fairness.png](./images/xgb_imputed_fairness.png) + +***Points to consider*** + +Performing the fairness assessment on the categories of interest gives additional insight into how the model performs by different patient categories of interest (by demographics, etc.). Future researchers should perform fairness assessments to better evaluate model performance, especially for models that may be deployed in a clinical setting. Other methods of assessing fairness include evaluating true positives, sensitivity, positive predictive value, etc. at various threshold across the different groups of interest, which would allow selection of a threshold that balances model performance across the groups of interest. + +### 6.3.2.9 Risk assessment + +***Steps for running the 8_xgb_imputed_risk_categories.ipynb script*** + +Note: Risk categorization is run on the non-calibrated model results. + +- Input: +``` +complete_fairness_data.pickle +2021_xgb_pooling_results_final_roc.csv +``` +- Output: +``` +2021_xgb_imputed_risk_cat.csv +``` +Step 1. Import libraries +``` +import numpy as np +import pandas as pd +import pickle + +import sys +#path to the functions directory +sys.path.append('../../onc_functions/') + +from risk import get_risk_categories + +print('python-' + sys.version) +import datetime +dte = datetime.datetime.now() +dte = dte.strftime("%Y%m%d") +``` +Step 2. Import the details from the fairness assessment +``` +with open('../complete_fairness_data.pickle', 'rb') as f: + dataset = pickle.load(f) +``` + +Step 3. Import the pooled results from the model +``` +pred_df = pd.read_csv('./roc_results/2021_xgb_pooling_results_final_roc.csv') +``` +Step 4. Merge the details with the results +``` +data = pred_df.merge(dataset, on=['usrds_id','died_in_90']) +``` + +Step 5. Calculate risk. The function **get_risk_categories** is imported from the /onc_functions/risk.py file. + +``` +def get_risk_categories(dataset, y_proba_col_name, y_true_col_name): + + test_x_pd = dataset[dataset.subset > 6].copy().sort_values(by = 'usrds_id') + del dataset + + df = test_x_pd.loc[:,[y_true_col_name,y_proba_col_name]] + + #construct the risk categories from the predicted score + df['risk_categories'] = pd.cut(df[y_proba_col_name], + bins=[-0.1, 0.09, 0.19, 0.29, 0.39, 0.49, 0.59, 0.69, 0.79, 0.89, 0.99], + labels=['0-0.09', '0.1-0.19', '0.2-0.29', '0.3-0.39', '0.4-0.49', + '0.5-0.59','0.6-0.69','0.7-0.79','0.8-0.89','0.9-0.99']) + + #loop through all the categories to get the predicted score + risk_list = [] + for name, c in df.groupby('risk_categories'): + risk_dict = {} + risk_dict['Risk Category'] = name + risk_dict['Count'] = c[y_true_col_name].shape[0] + risk_dict['Count Died in 90'] = c[y_true_col_name].sum() + risk_dict['Count Survived'] = c[y_true_col_name].shape[0]-c[y_true_col_name].sum() + risk_dict['Percent Died in 90'] = c[y_true_col_name].sum()/c[y_true_col_name].shape[0] + + risk_list.append(risk_dict) + + df_risk = pd.DataFrame(risk_list) + return df_risk +``` +Run the function above +``` +risk_cat = get_risk_categories(data, + y_proba_col_name='score', + y_true_col_name='died_in_90') +``` +Step 6. Save +``` +risk_cat.to_csv('./results/' + str(dte) + '_xgb_imputed_risk_cat.csv') +``` +![xgb_imputed_risk.png](./images/xgb_imputed_risk.png) + +## 6.3.3 Logistic Regression (LR) Model + +LR is a classic categorization model that can be used to examine the association of (categorical or continuous) independent variable(s) with one binary dependent variable. + +**Environment** + +The environment used for the LR model was purchased on Amazon Web Services (AWS): +``` +Name: m5.4xlarge +vCPU: 16 +GPU: 0 +Cores: 8 +Threads per core: 2 +Architecture: x86_64 +Memory: 64 GB +Operating System: Linux (Ubuntu 20.04 Focal Fossa) +Network Performance: 10 GB or less +Zone: US govcloud west +``` +The LR model takes less than 1 day to run each section of code if using the above environment. + +The LR model and cross validation methods from the Python (version 3.6.9) library scikit learn (version 0.24.1) were utilized along with the following libraries: + +|Python Library |Version| +|-------------------|---------| +|scikit-learn| 0.24.1| +|numpy |1.19.5| +|pandas |1.1.5| +|matplotlib |3.3.3| +|seaborn |0.11.1| + + +***Points to consider*** + +The use of parallel processing significantly decreased the amount of time it took to run the model. + +### 6.3.3.1 Pre-processing the training dataset + +The preprocessing included one hot encoding of categorical features and removal of features with missing values. + +***Steps for running the 1_lr_preprocessing.ipynb script*** + +- Input: +``` +medexpressesrd +micecomplete_pmm +numeric_columns.pickle +``` +- Output: +``` +complete1.pickle +complete2.pickle +complete3.pickle +complete4.pickle +complete5.pickle +``` + +Step 1. Install/import libraries +``` +import psycopg2 +import sqlalchemy +from sqlalchemy import create_engine + +# other libraries +import numpy as np +import pandas as pd +import sys +import pickle +import seaborn as sns + +#plotting +import matplotlib.pyplot as plt +%matplotlib inline +``` +Step 2. Connect to the Postgres database. + +The credentials for the Postgres database will be inserted here. +``` +con = create_engine('postgresql://username:password@address/dbname') +``` +Step 3. Get data + +Load the full non-imputed data found in the `medexpressesrd` table from Postgres database. +``` +df = pd.read_sql_query('''SELECT * FROM medxpreesrd;''', con) +``` +Get counts for each class. (This gets used later when we train the model.) +``` +neg_class_count, positive_class_count = np.bincount(df['died_in_90']) +``` +The labels are 2 integers, 0 (survived) or 1 (deceased). These correspond to the class. Note that we have a class imbalance with deceased being the minority class. +``` +Label Class Count +0 survived 1064112 +1 deceased 86083 +``` +![class_imbalance_bar.png](./images/class_imbalance_bar.png) +Step 4. Remove missing data + +Logistic regression models cannot account for missing values. The columns to remove are loaded at the top of the notebook in the variable **vars_to_remove** and were chosen by the clinical experts who are part of the project team. + +For this dataset, the columns of pre-esrd claims data that have missing values (claim counts, etc) were removed, keeping the binary features from the Medicare pre-ESRD claims, which include indicators for claims in each care setting, indicator for pre-ESRD claims, indicators for each diagnosis group. +``` +df.drop(columns=vars_to_remove,inplace=True) +``` + +Step 5. Encode categorical features + +One variable **dial_train_time** was created from taking the difference between 2 dates in the medevid table. This feature was the only non-claims-related feature to have a large number of missing values, but instead of dropping, it was encoded as follows: +- a number greater than zero=1 +- 0=0 +- missing=3 +Thus, the feature is turned into a categorical rather than numeric variable to retain some (though not all) information. +``` +df.dial_train_time = df.dial_train_time.fillna(-1) +df.dial_train_time=df.dial_train_time.astype(int).clip(lower=-1,upper=1) +df.dial_train_time=df.dial_train_time.astype(str).replace("-1","na") +``` +Use dummy variables for categorical variables (loaded at the top of the notebook) which is the method used for one-hot encoding. + +Get the list of categorical variables that have more then 2 levels, then encode using pandas get_dummies function. +``` +dummy_list = [] +for col in categoryVars: + u = len(df[col].unique()) + if u>2: + dummy_list.append(col) + +df = pd.concat([df, pd.get_dummies(df.loc[:,dummy_list].astype('str'))],axis=1).drop(columns=dummy_list,axis=1) +``` +Step 6. Load imputed data +Import imputed data `micecomplete_pmm` table from Postgres. +``` +imp = pd.read_sql_query('''SELECT *, row_number() + OVER(PARTITION BY usrds_id) AS impnum + FROM micecomplete_pmm +''', con) +``` +Step 7. Remove the 5 (imputed) columns from the original data +``` +df.drop(columns=["height", "weight", "bmi", "sercr", "album", "gfr_epi", "heglb"],inplace=True) +df.shape = (1150195, 290) +``` +Step 8. Separate the imputed data into 5 data frames + +This makes it easier to store, load, and compute. +``` +imp1 = imp[imp.impnum==1] +imp2 = imp[imp.impnum==2] +imp3 = imp[imp.impnum==3] +imp4 = imp[imp.impnum==4] +imp5 = imp[imp.impnum==5] +``` +Step 9. Merge the encoded data with each of the 5 imputed datasets. + +This is a left merge on the non-imputed data based on the **usrds_id** and the **subset** number. +``` +complete1 = pd.merge(df, imp1, how='left', on=["usrds_id","subset"]) +complete2 = pd.merge(df, imp2, how='left', on=["usrds_id","subset"]) +complete3 = pd.merge(df, imp3, how='left', on=["usrds_id","subset"]) +complete4 = pd.merge(df, imp4, how='left', on=["usrds_id","subset"]) +complete5 = pd.merge(df, imp5, how='left', on=["usrds_id","subset"]) + +complete5.shape +(1150195, 298) +``` +Step 10. Save + +Save each set to the current directory as a pickle file (a file type that works well for pandas dataframes). +``` +with open('complete1.pickle', 'wb') as picklefile: + pickle.dump(complete1, picklefile) +with open('complete2.pickle', 'wb') as picklefile: + pickle.dump(complete2, picklefile) +with open('complete3.pickle', 'wb') as picklefile: + pickle.dump(complete3, picklefile) +with open('complete4.pickle', 'wb') as picklefile: + pickle.dump(complete4, picklefile) +with open('complete5.pickle', 'wb') as picklefile: + pickle.dump(complete5, picklefile) +``` +***Points to consider*** + +The approach used to handle missing values is dependent on the dataset and the features in the dataset. Clinical expertise is crucial in understanding the impact of missing values and whether or not they should be imputed, removed, or replaced. + +### 6.3.3.2 Hyperparameter tuning and final logistic regression model + +This script computes the 5-fold cross-validation gridsearch on each set of the complete imputed data to find the best hyperparameters for the logistic regression model. LR has few parameters to set, therefore, hyperparameter tuning is simpler for this model. The data was split into test and train sets (the same ~70/30 split used in the other models). The training data was used to run the cross-validation model. The following parameters were tested: +- regularization strength +- regularization type (penalty) +- max iterations for convergence +- class weight + +The cross validation was run once per each imputed dataset and results were pooled (averaged). + +***Steps for running the 2_logistic_regression.ipynb script*** + +- Input: +``` +complete1.pickle +complete2.pickle +complete3.pickle +complete4.pickle +complete5.pickle +``` +- Output: +``` +2021_LR_cv_clf_imp_1.pickle +2021_LR_cv_clf_imp_2.pickle +2021_LR_cv_clf_imp_3.pickle +2021_LR_cv_clf_imp_4.pickle +2021_LR_cv_clf_imp_5.pickle +2021_final_LR_model_test_pred_proba_imp_x.pickle +2021_final_model_LR_fpr_all.pickle +2021_final_model_LR_tpr_all.pickle +2021_final_model_LR_auc_all.pickle +2021_final_LR_model.pickle +``` +Step 1. Install/import libraries +``` +import pandas as pd +import numpy as np +import os +import pickle +import sklearn.metrics as metrics +from sklearn.metrics import auc, plot_confusion_matrix, roc_curve, plot_roc_curve, accuracy_score, roc_auc_score, classification_report, confusion_matrix, PrecisionRecallDisplay, precision_recall_curve, RocCurveDisplay + +from sklearn.model_selection import GridSearchCV +from sklearn.preprocessing import StandardScaler + +from sklearn.model_selection import KFold, StratifiedKFold, train_test_split, GridSearchCV +from sklearn.preprocessing import StandardScaler +from sklearn.linear_model import LogisticRegressionCV, LogisticRegression +from sklearn.dummy import DummyRegressor, DummyClassifier + +import seaborn as sns +import matplotlib.pyplot as plt +``` +Step 2. Load numeric column list +``` +with open('numeric_columns.pickle', 'rb') as f: + nu_cols = pickle.load(f) +``` +Step 3. Import a set of data and scale numeric columns + +Each imputed set should be run separately, so only import one set at a time. Here, we import imputation 5. +``` + with open('complete5.pickle', 'rb') as f: + dataset = pickle.load(f) +``` +Keep only the training data subsets (1-6) since we are only running a cross validation to obtain the optimal parameters for the model. +``` + X_train = dataset[dataset.subset <= 6].copy().sort_values(by = 'usrds_id') +``` +Step 4. Separate the labels (typically denoted as 'y') and save as an array. +``` + y_train = np.array(X_train.pop('died_in_90')) +``` +Step 6. Scale only the numeric columns with an sklearn library **StandardScaler**. +``` + scaler = StandardScaler() + X_train[nu_cols] = scaler.fit_transform(X_train[nu_cols]) +``` +Step 5. Save the training data as an array (rather than a pandas dataframe) and drop the non-feature columns. +``` + X_train = np.array(X_train.drop(columns=['subset','usrds_id','impnum'])) +``` + +Step 6. Create the grid for the grid search +``` +param_grid = [{ + 'penalty':['l1','l2','elasticnet'], + 'C': np.logspace(-3, 3, 10, 20), + 'max_iter': [500, 1000, 1500], + 'class_weight' :['balanced'] + }] +``` + +Step 7. Instantiate the model +``` +lr_model = LogisticRegression() +clf = GridSearchCV( + lr_model, + param_grid=param_grid, + cv=5, + verbose=True, + n_jobs=-1, + scoring='average_precision' +) +``` +Step 8. Fit the grid search 5-fold cross-validated logistic regression model +``` +best_clf = clf.fit(X_train, y_train) +``` +Save the model. +``` +with open('2021_LR_cv_clf_imp_'+str(imp)+'.pickle', 'wb') as picklefile: + pickle.dump(clf,picklefile) +``` +Step 9. Calculate predictions on the test set +``` +pred_proba_onc_train = clf.predict_proba(X_train)[:,1] +``` +Step 10. Metrics and results +Calculate and plot the roc_auc (area under the receiver operating characteristic curve) for each fold. +``` +train_score = roc_auc_score(y_train, pred_proba_onc_train) +``` +Step 11. Import data and scale numeric cols + +Train and evaluate the final model based on the best parameters from the cross-validation step. This must be done for each of the 5 imputed datasets. + +Load a set of data. +``` +with open('./complete' + str(imp) + '.pickle', 'rb') as f: + dataset = pickle.load(f) + +``` +Separate the training set (1-6) +``` +train_x = dataset[dataset.subset <= 6].copy().sort_values(by = 'usrds_id') +``` +from the test set (7-9). +``` +test_x = dataset[dataset.subset > 6].copy().sort_values(by = 'usrds_id') +``` +Separate the labels. +``` +train_y = np.array(train_x.pop('died_in_90')) +test_y = np.array(test_x.pop('died_in_90')) +``` +Scale the numeric columns by training the model on the training set and then using this to transform the test set. Also remove the non-feature columns used to identify patients, imputations or subsets. +``` +scaler = StandardScaler() +train_x[nu_cols] = scaler.fit_transform(train_x[nu_cols]) +train_x = np.array(train_x.drop(columns=['subset','usrds_id','impnum'])) + +test_x[nu_cols] = scaler.transform(test_x[nu_cols]) +test_x = np.array(test_x.drop(columns=['subset','usrds_id','impnum'])) +``` + +Step 12. Instantiate the final logistic regression model + +Use the best hyperparameters from the cross-validation for the final model. +``` +lr_model_final = LogisticRegression(C=0.1, + penalty='l2', + max_iter=1000, + solver='saga', + class_weight='balanced', + n_jobs=-1, + verbose=1, + random_state=499) +``` +Step 13. Train the model + +Fit the model on the training data subsets. +``` +logistic_model_final = lr_model_final.fit(train_x, train_y) +``` +Step 14. Evaluate the model + +Evaluate the model by predicting on the test set and plot the outcome for each imputation. +``` +pred_proba_onc_test = logistic_model_final.predict_proba(test_x) +``` +Step 15. Save the model +``` +with open('2021_final_LR_model_test_pred_proba_imp_' + str(imp) + '.pickle', 'wb') as picklefile: + pickle.dump(pred_proba_onc_test, picklefile) +``` +***Points to consider*** + +1. Standardization allows for comparison of multiple features in different units and the penalty (i.e., L1) will be applied more equally across the features. The model will learn the importance of features better and faster when it isn't overwhelmed by a feature with a much larger range than the others. + +2. Logistic regression models do not perform well when the outcome variable is imbalanced (or heavily skewed towards one outcome). The outcome variables (survived, died_in_90) in the training dataset was balanced through weighting (edit the weight parameter in the model to give more weight to the minority class and less to the majority class). Balancing the data ensures that the models have sufficient data from both of the outcome classes (died vs survived) on which to train. This results in a better balance between sensitivity and specificity, which is important for this dataset where mortality is predicted. + +3. Due to the small set of hyperparameters to tune, this model does not require a GPU or even multiple CPUs to run the cross-validation. + +4. It is important to keep the test set separate when scaling, otherwise we are peeking at the test set which will cause an invalid evaluation of the model. + +### 6.3.3.3 Pool Results +***Steps for running the 3_pool_results_from_imputations.ipynb script*** + +This script pools the probability predicions for each row/patient from all 5 imputations by averaging the scores. + +- Input: +``` +2021_final_LR_model_test_pred_proba_imp_1.pickle +2021_final_LR_model_test_pred_proba_imp_2.pickle +2021_final_LR_model_test_pred_proba_imp_3.pickle +2021_final_LR_model_test_pred_proba_imp_4.pickle +2021_final_LR_model_test_pred_proba_imp_5.pickle +``` + +- Output: +``` +2021_final_LR_model_test_pred_proba_pooled.pickle +``` + +Step 1. Libraries +``` +import pickle +import numpy as np +import pandas as pd + +import psycopg2 +import sqlalchemy +from sqlalchemy import create_engine +``` + +Step 2. Import results from each imputation +``` +with open('./results/2021_final_LR_model_test_pred_proba_imp_1.pickle','rb') as f: + imp1_pred = pickle.load(f) +with open('./results/2021_final_LR_model_test_pred_proba_imp_2.pickle','rb') as f: + imp2_pred = pickle.load(f) +with open('./results/2021_final_LR_model_test_pred_proba_imp_3.pickle','rb') as f: + imp3_pred = pickle.load(f) +with open('./results/2021_final_LR_model_test_pred_proba_imp_4.pickle','rb') as f: + imp4_pred = pickle.load(f) +with open('./results/2021_final_LR_model_test_pred_proba_imp_5.pickle','rb') as f: + imp5_pred = pickle.load(f) +``` + +Step 3. Keep only the predictions from the positive class +``` +pooled = pd.DataFrame() +pooled['imp1']=imp1_pred[:,1] +pooled['imp2']=imp2_pred[:,1] +pooled['imp3']=imp3_pred[:,1] +pooled['imp4']=imp4_pred[:,1] +pooled['imp5']=imp5_pred[:,1] +``` +Step 4. Calculate the mean and standard deviation of the predicted probability for the positive class (died_in_90) for each patient/row. +``` +pooled['score'] = pooled.mean(axis=1) +pooled['std_'] = pooled.std(axis=1) +``` +![lr_pooled.png](./images/lr_pooled.png) + +Step 5. Import details from the medexpreesrd table +``` +con = create_engine('postgresql://username:password@location/dbname') + +dataset = pd.read_sql_query('''SELECT usrds_id, died_in_90, subset FROM medxpreesrd;''', con) +``` +Step 6. Sort the values so they are in the same order as when the LR was calculated and keep only the test set. +``` +dataset = dataset[dataset.subset > 6].copy().sort_values(by = 'usrds_id').reset_index(drop=True) +``` +Step 7. Merge the details with the pooled predictions. +``` +pooled = pooled.merge(dataset, left_index=True, right_index=True) +``` +Step 8. Save +``` +with open('./results/2021_final_LR_model_test_pred_proba_pooled.pickle', 'wb') as picklefile: + pickle.dump(pooled, picklefile) +``` + +### 6.3.3.4 Plot Results +***Steps for running the 4_plot_lr_roc_auc.ipynb script*** + +- Input: +``` +2021_final_LR_model_test_pred_proba_pooled.pickle +``` +- Output: +``` +lr_roc_auc_bw.png +2021_lr_confusion_matrix.csv +``` + +Step 1. Import libraries +``` +import pandas as pd +import numpy as np +import pickle + +import sys +#path to the functions directory +sys.path.append('../onc_functions/') + +#import custom plotting functions +from plot_functions import onc_calc_cm, onc_plot_roc +``` +Step 2. Load the pooled model results +``` +with open('./results/2021_final_LR_model_test_pred_proba_pooled.pickle','rb') as f: + results = pickle.load(f) + +results = results.loc[:,['score','died_in_90','subset','usrds_id']] +results = results.rename(columns={'died_in_90':'y'}) +``` +Step 3. Plot the ROC AUC. This function **onc_plot_roc** is located and imported from /onc_functions/plot_functions.py +``` +def onc_plot_roc(y_true, y_pred, model_name, **kwargs): + ''' + Plot the ROC AUC and return the test ROC AUC results. + INPUT: y_true, y_pred, model_name, **kwargs + ''' + + #calc values for plot + false_positives, true_positives, threshold = roc_curve(y_true, y_pred) + c_roc_auc_score = auc(false_positives, true_positives) + + #set figure params + fig1 = plt.figure(1, figsize=(12,30),dpi=400) + ax1 = plt.subplot2grid((7, 1), (0, 0), rowspan=2) + + #plot reference line for chance + ax1.plot([0, 1], [0, 1], linestyle='--', lw=2, color='gray', + label='Chance', alpha=.8) + + # plot AUC ROC + ax1.plot(false_positives, true_positives, + label=r'ROC (AUC = %0.3f)' % (c_roc_auc_score), + lw=2, alpha=.8, color = 'k') + + # additional figure params + ax1.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],) + ax1.legend(loc="lower right") + plt.xlabel('1-Specificity') + plt.ylabel('Sensitivity') + plt.rc('axes', labelsize=22) # fontsize of the x and y labels + plt.rc('xtick', labelsize=15) # fontsize of the tick labels + plt.rc('ytick', labelsize=15) # fontsize of the tick labels + plt.rc('legend', fontsize=20) # legend fontsize + # save plot + plt.savefig(model_name + "_roc_auc_bw.png", dpi=400, transparent=True) + plt.show() +``` +``` +onc_plot_roc( + y_true=results.y, + y_pred=results.score, + model_name='lr'); +``` +![lr_roc_auc_bw_nothreshold_hires_crop.png](./images/lr_roc_auc_bw_nothreshold_hires_crop.png) + +Step 4. Print and save the performance metrics at multiple thresholds +``` +def onc_calc_cm(y_true, y_predictions, range_probas=[0.1,0.5]): + ''' + Plot the confusion matrix and scores for multiple thresholds + ''' + df = pd.DataFrame(index = range_probas, + columns=['threshold','sensitivity','specificity', + 'likelihood_ratio_neg','likelihood_ratio_pos', + 'tp','fp','tn','fn','total_survived','total_deceased',]) + for proba_threshold in range_probas: + + cm = confusion_matrix(y_true, y_predictions > proba_threshold) + tn = cm[0][0] + fp = cm[0][1] + + sensitivity = recall_score(y_true, y_predictions > proba_threshold) + specificity = tn / (tn + fp) + + df.loc[proba_threshold, "threshold"] = proba_threshold + df.loc[proba_threshold,"sensitivity"] = sensitivity + df.loc[proba_threshold, "specificity"] = specificity + df.loc[proba_threshold, "likelihood_ratio_neg"] = (1-sensitivity)/specificity + df.loc[proba_threshold, "likelihood_ratio_pos"] = sensitivity/(1-specificity) + df.loc[proba_threshold, "tp"] = cm[1][1] + df.loc[proba_threshold, "fp"] = fp + df.loc[proba_threshold, "tn"] = tn + df.loc[proba_threshold, "fn"] = cm[1][0] + df.loc[proba_threshold, "total_survived"] = np.sum(cm[0]) + df.loc[proba_threshold, "total_deceased"] = np.sum(cm[1]) + return df +``` +``` +cm = onc_calc_cm( + results.y, + results.score, + range_probas=[.10,.20, .30, .40, .50]) +cm.to_csv('./results/2021_lr_confusion_matrix.csv') +cm +``` +![lr_cm.png](./images/lr_cm.png) +### 6.3.3.5 Feature Importance +***Steps for running the 5_lr_feature_importance.ipynb script*** + +Plot the feature importance according to the final logistic regression model. +- Input: +``` +complete5.pickle +2021_final_LR_model +``` +- Output: +``` +2021_top_bottom_plot.svg +2021_top_log_regression_coef_20.csv +``` + +Step 1. Load the final model +``` +with open('./results/2021_final_LR_model.pickle', 'rb') as picklefile: + logistic_model_final = pickle.load(picklefile) +``` +Step 2. Import feature names +Import one imputation of the data to get feature names. +``` +with open('./complete5.pickle', 'rb') as f: + dataset = pickle.load(f) +feats = dataset.iloc[0:1,:] +``` +Drop the columns that were not used as features in the model. +``` +ff = feats.drop(columns=['usrds_id','subset','died_in_90','impnum']).copy() +ff = ff.columns +ff = np.array(ff) +``` +Step 3. Sort features by coeffecient score + +Create a function to sort the features by their highest (tops) or lowest (bottom) coeffecient to see which features were most important for the model to determine which class a patient was in. Here we get the strongest scores regardless of whether they are positive or negative because we are interested in the magnitude (difference from zero). +``` +def get_most_important_features(r, model, n=5): + + classes ={} + for class_index in range(model.coef_.shape[0]): + word_importances = [(el, r[i]) for i, el in enumerate(model.coef_[class_index])] + sorted_coeff = sorted(word_importances, key = lambda x : x[0], reverse=True) + tops = sorted(sorted_coeff[:n], key = lambda x : x[0]) + bottom = sorted_coeff[-n:] + classes[class_index] = { + 'tops':tops, + 'bottom':bottom + } + return classes +```` +Call function for the top and bottom 20 features. +``` +importance = get_most_important_features(ff, logistic_model_final, 20) +``` +Save the strongest scores (most negative and most positive). +``` +top_scores = [a[0] for a in importance[0]['tops']] +top_words = [a[1] for a in importance[0]['tops']] +bottom_scores = [a[0] for a in importance[0]['bottom']] +bottom_words = [a[1] for a in importance[0]['bottom']] +top_coef = pd.DataFrame(columns=['vocab','coef']) +top_coef['vocab'] = top_words + bottom_words +top_coef['coef'] = top_scores + bottom_scores +top_coef = top_coef.sort_values(by='coef',axis=0,ascending=False) +top_coef.to_csv('./results/2021_top_log_regression_coef_20.csv') +``` + +![lr_feat_importance.png](./images/lr_feat_importance.png) + +### 6.3.3.6 Fairness assessment +ML models can perform differently for different categories of patients, so the LR model was assessed for fairness, or how well the model performs for each category of interest (demographics—sex, race, and age—as well as initial dialysis modality). Age is binned into the following categories based on clinician input and an example in literature: 18-25, 26-35, 36-45, 46-55, 56-65, 66-75, 76-85, 86+. The USRDS predefined categories for race, sex, and dialysis modality were used for the fairness assessment. + +***Steps for running the 6_logistic_regression_fairness.ipynb script*** + +This script calculates the ROC AUC for specific groups of patients to assess the fairness of the final model. + +- Input: +``` +medexpreesrd +2021_final_LR_model_test_pred_proba_pooled.pickle +``` +- Output: +``` +complete_fair1.pickle +2021_lr_fairness.csv +``` +Step 1. Import libraries +``` +import numpy as np +import pandas as pd +import sys +import pickle + +#path to the functions directory +sys.path.append('../onc_functions/') + +from fairness import get_fairness_assessment + +import datetime +dte = datetime.datetime.now() +dte = dte.strftime("%Y") + +print('python-' + sys.version) +``` + +Step 2. Get the columns of data required to compute fairness assessment and save them. +``` +con = create_engine('postgresql://username:password@location/dbname') +df = pd.read_sql_query('''SELECT usrds_id, died_in_90, inc_age, sex, dialtyp, race, hispanic, subset FROM medxpreesrd;''', con) +``` +Step 3. Save +``` +with open('complete_fair1.pickle', 'wb') as picklefile: + pickle.dump(df, picklefile) +``` +Step 4. Import the pooled results from the LR model +``` +with open('./results/2021_final_LR_model_test_pred_proba_pooled.pickle', 'rb') as f: + y_pred = pickle.load(f) +``` +Step 5. Merge the fairness details with the results +``` +data = df.merge(y_pred, on=['usrds_id','died_in_90','subset']) +``` + +Step 6. Calculate fairness. The function **get_fairness_assessment** is imported from the /onc_functions/fairness.py file. This function calculates AUC and the confusion matrix from the model prediction scores for specific groups. +``` +def get_fairness_assessment(data, + y_proba_col_name, + y_true_col_name): + + #turn the continuous age variable into age categories + df['agegroup'] = pd.cut(df.inc_age, + bins=[17, 25, 35, 45, 55, 65, 75, 85, 90], + labels=[1, 2, 3, 4, 5, 6, 7, 8]) + + df = df.drop(columns=['inc_age']) + + #replace NaNs with a large number that does not appear in the data, effectively creating another category for missing values + df.loc[:,['race','dialtyp','hispanic']] = df.loc[:,['race','dialtyp','hispanic']].fillna(100.0, axis=1).copy() + + #Identify the cols for the fairness assessment + fairness_cols = ['agegroup', 'sex','dialtyp', 'race','hispanic'] + + #loop through all categories and values to get counts, auc, and confusion matrix + rows_list = [] + for col in fairness_cols: + for name, c in df.groupby(col): + fairness_dict = {} + fairness_dict['Feature'] = col + fairness_dict['Value'] = name + fairness_dict['Count'] = c.shape[0] + + fairness_dict['AUC'] = roc_auc_score(c[y_true_col_name], c[y_proba_col_name]) + tn, fp, fn, tp = confusion_matrix(y_true = c[y_true_col_name], + y_pred = np.where(c[y_proba_col_name] >= 0.5, 1, 0)).ravel() + fairness_dict['TN'] = tn + fairness_dict['FP'] = fp + fairness_dict['FN'] = fn + fairness_dict['TP'] = tp + rows_list.append(fairness_dict) + + #convert results from a list to a dataframe + df_fairness = pd.DataFrame(rows_list) + return df_fairness +``` +Step 7. Calculate the assessment and save the results. +``` +fairness = get_fairness_assessment(data, + y_proba_col_name='score', + y_true_col_name='died_in_90') +fairness.to_csv('./results/2021_lr_fairness.csv') +``` + +![lr_fair.png](./images/lr_fair.png) + +***Points to consider*** + +Performing fairness assessment on the categories of interest gives additional insight into how the model performs by different patient categories of interest (by demographics, etc.). Future researchers should perform fairness assessments to better evaluate model performance, especially for models that may be deployed in a clinical setting. Other methods of assessing fairness include evaluating true positives, sensitivity, positive predictive value, etc. at various threshold across the different groups of interest, which would allow selection of a threshold that balances model performance across the groups of interest. + +## 6.3.3.7 Risk Assessment + +***Steps for running the 7_logistic_regression_risk.ipynb script*** + +- Input: +``` +complete_fair1.pickle +2021_final_LR_model_test_pred_proba_pooled.pickle +``` +- Output: +``` +2021_lr_risk_cat.csv +``` +Step 1. Import libraries +``` +import numpy as np +import pandas as pd +import pickle + +import sys +#path to the functions directory +sys.path.append('../onc_functions/') + +from risk import get_risk_categories + +print('python-' + sys.version) +import datetime +dte = datetime.datetime.now() +dte = dte.strftime("%Y%m%d") +``` + +Step 2. Import the details from the fairness assessment +``` +with open('complete_fair1.pickle','rb') as f: + details = pickle.load(f) +``` + +Step 3. Import the pooled results from the LR model +``` +with open('./results/2021_final_LR_model_test_pred_proba_pooled.pickle', 'rb') as f: + y_pred = pickle.load(f) +``` +Step 4. Merge the details with the results +``` +data = df.merge(y_pred, on=['usrds_id','died_in_90','subset']) +``` + +Step 5. Calculate risk. The function **get_risk_categories** is imported from the /onc_functions/risk.py file. + +``` +def get_risk_categories(dataset, y_proba_col_name, y_true_col_name): + + test_x_pd = dataset[dataset.subset > 6].copy().sort_values(by = 'usrds_id') + del dataset + + df = test_x_pd.loc[:,[y_true_col_name,y_proba_col_name]] + + #construct the risk categories from the predicted score + df['risk_categories'] = pd.cut(df[y_proba_col_name], + bins=[-0.1, 0.09, 0.19, 0.29, 0.39, 0.49, 0.59, 0.69, 0.79, 0.89, 0.99], + labels=['0-0.09', '0.1-0.19', '0.2-0.29', '0.3-0.39', '0.4-0.49', + '0.5-0.59','0.6-0.69','0.7-0.79','0.8-0.89','0.9-0.99']) + + #loop through all the categories to get the predicted score + risk_list = [] + for name, c in df.groupby('risk_categories'): + risk_dict = {} + risk_dict['Risk Category'] = name + risk_dict['Count'] = c[y_true_col_name].shape[0] + risk_dict['Count Died in 90'] = c[y_true_col_name].sum() + risk_dict['Count Survived'] = c[y_true_col_name].shape[0]-c[y_true_col_name].sum() + risk_dict['Percent Died in 90'] = c[y_true_col_name].sum()/c[y_true_col_name].shape[0] + + risk_list.append(risk_dict) + + df_risk = pd.DataFrame(risk_list) + return df_risk +``` +Run the function above +``` +risk_cat = get_risk_categories(data, + y_proba_col_name='score', + y_true_col_name='died_in_90') +``` +Step 6. Save +``` +risk_cat.to_csv('./results/' + str(dte) + '_lr_risk_cat.csv') +``` + +![lr_risk.png](./images/lr_risk.png) + +## 6.3.4 Artificial Neural Network--Multilayer Perceptron (MLP) Model + +This section containts the code for the artificial neural net model - multilayer perceptron: MLP. The repository contains ipython notebooks to train an artificial neural net to classify patients as `survived` or `died_in_90`. Tensorflow is the library used to create and train the neural network. For a more detailed explanation of neural networks or tensorflow, we recommend the tutorials at [https://www.tensorflow.org/tutorials](https://www.tensorflow.org/tutorials). + +**Environment** + +These ipython notebooks can easily be run in a plain tensorflow docker container or used on their own in a conda or other ipython environment. + +The environment used for the MLP model was purchased on Amazon Web Services (AWS): +``` +Name: p2.16xlarge +vCPU: 64 +Cores: 32 +Threads per core: 2 +Architecture: x86_64 +Memory: 732 GB +GPU: 16 +GPU Memory: 12 GB +GPU Manufacturer: NVIDIA +GPU Name: K80 +Operating System: Linux (Ubuntu 20.04 Focal Fossa) +Network Performance: 25 GB +Zone: US govcloud west +``` +This code takes approximately 3 days to run all the sections. + +The MLP model was trained using the python (version 3.6.9) tensorflow (version 2.4.1) library and cross validation methods were from the Python (version 3.6.9) library scikit learn (version 0.24.1) and the following libraries: +|Python library | Version| +|---------------|--------| +|tensorflow |2.4.1| +|scikit-learn |0.24.1| +|numpy| 1.19.5| +|pandas| 1.1.5| +|matplotlib| 3.3.3| + +***Points to consider*** + +An instance with GPUs can be utilized for tuning a large number of hyperparameters, as done here. If multiple cores or a GPU are not available, recommend choosing only a few hyperparameters to tune at one time. + +### 6.3.4.1 Run Docker container (optional) +Run the following command in the same directory where you store the jupyter notebook (the repository). This will first pull the container, then instantiate it and mount the current directory to allow you to access the notebooks and also save any changes. + +Step 1. Use the following code to run the container on your local machine: +``` +sudo docker run -it -v "$PWD":/tf/notebooks --name onc_tf_1 tensorflow/tensorflow:latest-jupyter +``` +Step 2. Then open a browser and go to the default port mapping +``` +localhost:8888 +``` +Step 3. There you will see this directory of notebooks that you can open and run. + +### 6.3.4.2 Run on a server (i.e. AWS) +Ports will need to be mapped when running the container. +In our case 8080 is the port that is open and we can access it from the local machine at that port if connected to the server +``` +sudo docker run -it -p 8080:8888 -v "$PWD":/tf/notebooks --name onc_tf_1 tensorflow/tensorflow:latest-jupyter +``` +To utilize a GPU (which can be useful for the cross-validation step). +``` +sudo docker run -it -p 8080:8888 -v "$PWD":/tf/notebooks --name onc_tf_gpu tensorflow/tensorflow:latest-gpu-jupyter +``` +Then you can access the current directory and notebooks at https://[my_aws_ec2_address]:8080 + +***Points to consider*** + +To run the cross-validation hyperparameter tuning with a GPU, run it from a python file (a conda environment worked) not the ipython jupyter notebook. + +### 6.3.4.3 Pre-processing the data +The preprocessing is the same as for the logistic regression model and includes one hot encoding of categorical features and removal of features with missing values in lieu of the binary features. + +***Steps for running the 1_mlp_preprocessing.ipynb script*** + +- Input: +``` +medexpressesrd +micecomplete_pmm +numeric_columns.pickle +``` +- Output: +``` +complete1.pickle +complete2.pickle +complete3.pickle +complete4.pickle +complete5.pickle +``` + +Step 1. Install and import libraries + +Install the python packages into the docker container (if running in a container) and import them into the notebook. +``` +!pip install --upgrade pip +!pip install --upgrade scikit-learn +!pip install pandas +!pip install psycopg2-binary +!pip install sqlalchemy +!pip install seaborn + +import psycopg2 +import sqlalchemy +from sqlalchemy import create_engine + +# other libraries +import numpy as np +import pandas as pd +import sys +import pickle + +#plotting +import matplotlib.pyplot as plt +import seaborn as sns +%matplotlib inline +``` +Step 2. Connect to the Postgres database + +The credentials for the Postgres database will be inserted here. +``` +con = create_engine('postgresql://username:password@address/dbname') +``` + +Step 3. Get data + +Load the full non-imputed data found in the `medexpressesrd` table from Postgres database. +``` +df = pd.read_sql_query('''SELECT * FROM medxpreesrd;''', con) +``` +Get counts for each class (this gets used lated when we train the model) +``` +neg_class_count, positive_class_count = np.bincount(df['died_in_90']) +``` +The labels are 2 integers, 0 (survived) or 1 (deceased). These correspond to the class. Note that we have a class imbalance with deceased being the minority class. +``` +Label Class Count +0 survived 1064112 +1 deceased 86083 +``` +![class_imbalance_bar.png](./images/class_imbalance_bar.png) + +Step 4. Remove missing data + +Neural networks models cannot handle missing values. The columns to remove are loaded at the top of the notebook in the variable vars_to_remove and were chosen based on clinician input. + +For this dataset, the columns of pre-ESRD claims data that have missing values (claim counts, etc) were removed but kept the binary features from the Medicare pre-ESRD claims, which include indicators for claims in each setting, indicator for pre-ESRD claims, and indicators for each diagnosis group. +``` +df.drop(columns=vars_to_remove,inplace=True) +``` + +Step 5. Encode categorical features + +One variable **dial_train_time** was created from taking the difference between 2 dates in the medevid table. This feature was the only non-claims-related feature to have a large number of missing values, but we did not want to drop it so we encoded it in the following way +- a number greater than zero=1 +- 0=0 +- missing=3 + +Thus, the feature is turned into a categorical rather than numeric variable to retain some (though not all) information. +``` +df.dial_train_time = df.dial_train_time.fillna(-1) +df.dial_train_time=df.dial_train_time.astype(int).clip(lower=-1,upper=1) +df.dial_train_time=df.dial_train_time.astype(str).replace("-1","na") +``` +Use dummy variables for categorical variables (loaded at the top of the notebook). + +Get the list of categorical variables that have more then 2 levels, then encode using pandas get_dummies function. +``` +dummy_list = [] +for col in categoryVars: + u = len(df[col].unique()) + if u>2: + dummy_list.append(col) + +df = pd.concat([df, pd.get_dummies(df.loc[:,dummy_list].astype('str'))],axis=1).drop(columns=dummy_list,axis=1) +``` +Step 6. Load imputed data + +Import imputed data `micecomplete_pmm` table from Postgres. +``` +imp = pd.read_sql_query('''SELECT *, row_number() + OVER(PARTITION BY usrds_id) AS impnum + FROM micecomplete_pmm +''', con) +``` +Step 7. Remove the 5 columns from the original data +``` +df.drop(columns=["height", "weight", "bmi", "sercr", "album", "gfr_epi", "heglb"],inplace=True) +df.shape = (1150195, 290) +``` +Step 8. Separate the imputed data into 5 dataframes + +Separating the 5 imputed datasets into different dataframes makes it easier to store, load, and compute. +``` +imp1 = imp[imp.impnum==1] +imp2 = imp[imp.impnum==2] +imp3 = imp[imp.impnum==3] +imp4 = imp[imp.impnum==4] +imp5 = imp[imp.impnum==5] +``` +Step 9. Merge the encoded data with each of the 5 imputed datasets + +This is a left merge on the non-imputed data based on the **usrds_id** and the **subset** number. +``` +complete1 = pd.merge(df, imp1, how='left', on=["usrds_id","subset"]) +complete2 = pd.merge(df, imp2, how='left', on=["usrds_id","subset"]) +complete3 = pd.merge(df, imp3, how='left', on=["usrds_id","subset"]) +complete4 = pd.merge(df, imp4, how='left', on=["usrds_id","subset"]) +complete5 = pd.merge(df, imp5, how='left', on=["usrds_id","subset"]) + +complete5.shape +(1150195, 298) +``` +Step 10. Save the data + +Save each set to the current directory as a pickle file (a file type that works well for pandas dataframes). +``` +with open('complete1.pickle', 'wb') as picklefile: + pickle.dump(complete1, picklefile) +with open('complete2.pickle', 'wb') as picklefile: + pickle.dump(complete2, picklefile) +with open('complete3.pickle', 'wb') as picklefile: + pickle.dump(complete3, picklefile) +with open('complete4.pickle', 'wb') as picklefile: + pickle.dump(complete4, picklefile) +with open('complete5.pickle', 'wb') as picklefile: + pickle.dump(complete5, picklefile) +``` +***Points to consider*** + +The approach used to handle missing values is dependent on the dataset and the features in the dataset. Clinical expertise is crucial in understanding the impact of missing values and whether or not they should be imputed, removed, or replaced. + +### 6.3.4.4 Hyperparameter tuning + +This script computes the 5-fold cross-validation gridsearch to find the best hyperparameters for the MLP model. Keras (tensorflow) is used to create the layers of the neural net and take advantage of multiple GPUs (if available). The gridsearch cross-validation is computed using with sci-kit learn GridSearchCV. The class imbalance is handled by using the class_weights parameter. This hyperparameter cannot be tuned as part of the grid search and the function mlp_cv must be run for each set of weights. + +***Steps for running the 2_mlp_cross_val.ipynb script*** + +- Input: +``` +build_mlp.py +complete1.pickle +complete2.pickle +complete3.pickle +complete4.pickle +complete5.pickle +numeric_columns.pickle +``` + +-Output: x = [1,2,3,4,5] +``` +2021_grid_best_params_imp_x_weight_m.pickle +2021_grid_best_auc_imp_x_weight_m.pickle +2021_grid_cv_results_imp_x_weight_m.pickle + +2021_grid_best_params_imp_x_weight_5.pickle +2021_grid_best_auc_imp_x_weight_5.pickle +2021_grid_cv_results_imp_x_weight_5.pickle + +2021_grid_best_params_imp_x_weight_10.pickle +2021_grid_best_auc_imp_x_weight_10.pickle +2021_grid_cv_results_imp_x_weight_10.pickle + +2021_grid_best_params_imp_x_weight_20.pickle +2021_grid_best_auc_imp_x_weight_20.pickle +2021_grid_cv_results_imp_x_weight_20.pickle +``` +Step 1. Import packages + +Tensorflow is a popular framework for training a neural network and keras is a high-level API used for ease of access to the tensorflow functions. +``` +!pip install --upgrade scikit-learn +!pip install pandas +!pip install pyyaml h5py +!pip install seaborn + +# TensorFlow and tf.keras +import tensorflow as tf +from tensorflow.keras import layers +from tensorflow.keras.wrappers.scikit_learn import KerasClassifier +from tensorflow.keras.optimizers import Adam, SGD, Adamax + +import sklearn.metrics as metrics +from sklearn.metrics import auc, plot_roc_curve, roc_curve, mean_squared_error, accuracy_score, roc_auc_score, classification_report, confusion_matrix, log_loss +from sklearn.model_selection import GridSearchCV +from sklearn.preprocessing import StandardScaler +import sklearn + +# load custom function for building the NN +import sys +sys.path.append('../onc_functions/') +from build_mlp import build_mlp + +# other libraries +import numpy as np +import pandas as pd +import sys +import pickle + +#plotting +import matplotlib.pyplot as plt +import seaborn as sns +%matplotlib inline +``` +Step 2. Import list of numeric columns + +The following code snippet obtains the list of numeric columns to be scaled +``` +with open('numeric_columns.pickle', 'rb') as f: + nu_cols = pickle.load(f) +``` +Step 3. Import a set of data and scale numeric columns + +Each imputed set should be run separately, so only import one set at a time. The example below imports imputation 5: +``` +with open('complete5.pickle', 'rb') as f: + dataset = pickle.load(f) +``` +Keep only the training data subsets (1-6) since we are only running a cross validation to obtain the optimal parameters for the model. +``` +X_train = dataset[dataset.subset <= 6].copy().sort_values(by = 'usrds_id') +``` +Separate the labels (typically denoted as 'y') and save as an array. +``` +y_train = np.array(X_train.pop('died_in_90')) +``` +Scale only the numeric columns with an sklearn library **StandardScaler**. +``` +scaler = StandardScaler() +X_train[nu_cols] = scaler.fit_transform(X_train[nu_cols]) +``` +Save the training data as an array (rather than a pandas dataframe) and drop the non-feature columns. +``` + X_train = np.array(X_train.drop(columns=['subset','usrds_id','impnum'])) +``` + +Step 4. Create the grid for the grid search + +Insert the different parameters you want to test when doing the cross validation. For a detailed explanation of each parameter, see the section on the *Build_mlp.py* script below. +``` + neurons = [16, 32, 64, 128] + layers = [1, 2] + kernel_regularizer = ['l2'] + dropout_rate = [ 0.1, 0.2, 0.4, 0.5, 0.6] + learn_rate = [.001, .0001, .0002] + activation = ['relu', 'sigmoid', 'tanh'] + optimizer = ['Adam'] + output_bias = [None] + epochs = [10, 20] # 1mill/256=4000 steps for one pass thru dataset + batches = [512, 256] +``` +Step 5. Set parameters for early stopping (to optimize time) + +Early stopping for the epochs based on the auc_pr (area under the precision-recall curve). +``` +early_stopping = tf.keras.callbacks.EarlyStopping( + monitor='auc_pr' , + verbose=1, + patience=10, + mode='max', + restore_best_weights=True) +``` + +Step 6. Build layers + +Use the Keras wrapper for scikitlearn and our custom build_mlp function imported from the custom python script build_mlp.py +``` + weighted_model_skl = KerasClassifier(build_fn=build_mlp, + verbose=0) +``` +***Points to consider*** + +Standardization and scaling of numeric features allows for comparison of multiple features in different units. The model will learn the importance of features better and faster when it is not overwhelmed by a feature with a much larger range than the others. + +### 6.3.4.5 Building layers and compiling the model +This script is used to create the neural network by building the layers and then compiling the model. This model will consist of a few simple layers (Dense and Dropout) to illustrate the concepts and give an example. + +**Wrapper function** + +In the actual notebook, the full code is wrapped in a function (**mlp_cv**) for ease of running the cross-validation with the different weightings and each imputation dataset. An example of the code for imputation 5 is included below: +``` +mlp_cv(class_weight_20, weight_name=20, imputation=5) +``` +***Steps for running the build_mlp.py script*** + +Step 1. Import tensorflow packages to create the layers of the network. +``` +import tensorflow as tf +from tensorflow.keras.layers import Dense, Dropout +from tensorflow.keras.layers.experimental import preprocessing +from tensorflow.keras.wrappers.scikit_learn import KerasClassifier +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.constraints import max_norm +from tensorflow.keras.metrics import AUC +``` +Step 2. Select metrics to measure the loss and the accuracy of the model. + +Multiple metrics can be reported as we have done here. +``` +METRICS = [ + tf.keras.metrics.TruePositives(name='tp'), + tf.keras.metrics.FalsePositives(name='fp'), + tf.keras.metrics.TrueNegatives(name='tn'), + tf.keras.metrics.FalseNegatives(name='fn'), + tf.keras.metrics.BinaryAccuracy(name='accuracy'), + tf.keras.metrics.Precision(name='precision'), + tf.keras.metrics.Recall(name='recall'), + tf.keras.metrics.AUC(name='auc'), + tf.keras.metrics.AUC(name='auc_pr', + num_thresholds=200, + curve="PR", + summation_method="interpolation", + dtype=None, + thresholds=None, + multi_label=False, + label_weights=None) +] +```` +Step 3. Define a function to take in the parameters for building the network. +``` +def build_mlp( + layers=2, + neurons=16, + output_bias=None, + optimizer='Adam', + activation='relu', + learn_rate=.0002, + dropout_rate=0.2, + kernel_regularizer='l2', + metrics=METRICS +): + if output_bias is not None: + output_bias = tf.keras.initializers.Constant(output_bias) + model = tf.keras.Sequential() +``` +This loop allows for us to test one or more identical dense layers based on the **layers** argument. Dense or fully connected layers consist of the number of neurons(nodes) defined in the **neurons** argument, an activation function defined by **activation**, an input shape of 294 (this is specific to our data since we always have 294 features or columns. If a feature is added or removed, this number needs to reflect that), and a kernel regularizer defined by the **kernel_regularizer** argument. +``` +for i in range(layers): + model.add(Dense( + neurons, + activation=activation, + input_shape=(294,), + kernel_regularizer=kernel_regularizer)) +``` +A Dropout layer is used to avoid overfitting when testing the model. The parameter **dropout_rate** determines the rate. +``` +model.add(Dropout(dropout_rate)) +``` +The final layer is a dense layer with a sigmoid activation function to give the probablility of each class for each sample. +``` +model.add(Dense( + 1, + activation='sigmoid', + bias_initializer=output_bias)) +``` +Step 4. Set the final arguments for compiling the model. +- Optimizer —This is how the model is updated based on the data it sees and its loss function. +- Loss function —This measures how accurate the model is during training. You want to minimize this function to "steer" the model in the right direction. BinaryCrossentropy() is used because we are training a binary classification model. +- Metrics —Used to monitor the training and testing steps. Here we use multiple metrics that are printed out. +``` + opt = optimizer(lr=learn_rate) + + model.compile( + optimizer=opt, + loss=tf.keras.losses.BinaryCrossentropy(), + metrics=metrics) + + return model +``` +Step 5. Instantiate the grid search cross validation model + +Evaluate our list of parameters using the sci-kit learn package **GridSearchCV** to run a 5-fold cross validation. This will result in the best combination of these parameters according to our score (which is set to **average_precision** to optimize precision and recall). Multiple processors can be taken advantage of by setting n_jobs=-1. Training many different parameters will take a significant amount of time that depends on the number of processors and size of the data. +``` +grid = GridSearchCV( + weighted_model_skl, + param_grid=params, + cv=5, + scoring='average_precision', + return_train_score=True, + n_jobs=-1 + ) +``` + +Step 6. Class imbalance and fit the model to the data + +When fitting the model to the training data, the class imbalanced can be set using the **class_weight** parameter. +``` +grid_result = grid.fit( + X_train, + y_train, + class_weight=selected_class_weight, + callbacks=[early_stopping] + ) +``` +Note that when testing multiple class weights, this must be done by fitting a new cross-validated model (running the entire script) for each different set of class weights to be tested, rather than as a hyperparameter in the model. +Multiple class weights were tested on the data. +- Classes: 0=survived (negative class), 1=died_in_90 (positive class). +``` +total = 1150195 +positive_class_count = 86083 #(7.48% of total) +neg_class_count = 1064112 #(92.52% of total) +# Scaling by total/2 helps keep the loss to a similar magnitude. +# The sum of the weights of all examples stays the same. +weight_for_0 = (1 / neg_class_count)*(total)/2.0 +weight_for_1 = (1 / positive_class_count)*(total)/2.0 + +class_weight_m = {0: weight_for_0, 1: weight_for_1} +``` +- A range of stronger weights the minority class. +``` +class_weight_5 = {0: 1, 1: 5} +class_weight_10 = {0: 1, 1: 10} +class_weight_20 = {0: 1, 1: 20} +``` + +Step 7. Summarize and print results +``` +means = grid_result.cv_results_['mean_test_score'] +stds = grid_result.cv_results_['std_test_score'] +params = grid_result.cv_results_['params'] + +for mean, stdev, param in zip(means, stds, params): + print("%f (%f) with: %r" % (mean, stdev, param)) +print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_)) +``` + +Step 8. Save results + +Save the result for each set of class weight parameters (to select the one with the best score) and for each dataset (each imputation) and review the best_params_ from each one to check for similarity. +``` + with open('./results/2021_grid_best_params_imp_' + str(imputation) + '_weight_' + str(weight_name) + '.pickle', 'wb') as f: + pickle.dump(grid_result.best_params_, f) + + with open('./results/2021_grid_best_auc_imp_' + str(imputation) + '_weight_' + str(weight_name) + '.pickle','wb') as f: + pickle.dump(grid_result.best_score_, f) + + with open('./results/2021_grid_cv_results_imp_' + str(imputation) + '_weight_' + str(weight_name) + '.pickle','wb') as f: + pickle.dump(grid_result.cv_results_, f) +``` +***Points to consider*** + +1. One imputed set of data was used to tune hyperparameters which reduced computational time while effectively tuning the parameters. + +2. Due to the severe class imbalance in the dataset, the AUC ROC tends to be high while recall is low. It is well-known that precision-recall plots are more informative than AUC ROC plots when training a binary classification model on severely imbalanced data; therefore, the average precision metric from sklearn (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html#sklearn.metrics.average_precision_score) was used to tune the MLP model. + +![average_precision](./images/avg_prec.png) + +### 6.3.4.6 Final MLP Model +***Steps for running the 3_mlp_final_model.ipynb script*** + +This script trains and evaluates the final MLP model based on the best parameters from the hyperparameter tuning/cross-validation step. + +- Input: +``` +complete1.pickle +complete2.pickle +complete3.pickle +complete4.pickle +complete5.pickle +numeric_columns.pickle +``` + +- Output: x=[1-5] +``` +2021_MLP_final_results_imp_x.pickle +2021_MLP_final_model_imp_x.h5 +2021_MLP_final_eval_imp_x.pickle + +2021_MLP_final_fpr.pickle +2021_MLP_final_tpr.pickle +2021_MLP_final_auc.pickle + +2021_MLP_final_ytest_all.pickle +2021_MLP_final_ypred_all.pickle + +2021_MLP_final_prec.pickle +2021_MLP_final_recall.pickle +2021_MLP_final_avgprec_thresh.pickle +2021_MLP_final_avgprec_score.pickle +``` +Step 1. Install and import libraries +``` +!pip install --upgrade scikit-learn +!pip install pandas +!pip install pyyaml h5py +!pip install seaborn + +# TensorFlow and tf.keras +import tensorflow as tf +from tensorflow.keras.layers import Dense, Dropout +from tensorflow.keras.optimizers import Adam + +import sklearn +from sklearn.metrics import auc, average_precision_score, roc_curve, precision_recall_curve, roc_auc_score, confusion_matrix +from sklearn.preprocessing import StandardScaler + +# other libraries +import numpy as np +import pandas as pd +import sys +import pickle + +#plotting +import matplotlib.pyplot as plt +import seaborn as sns +%matplotlib inline +``` +Step 2. Import list of numeric columns + +Get list of columns to be scaled +``` +with open('numeric_columns.pickle', 'rb') as f: + nu_cols = pickle.load(f) +``` +Step 3. Import data + +Import the data from a single imputation. +``` +imp=5 +with open('complete' + str(imp) + '.pickle', 'rb') as f: + dataset = pickle.load(f) +``` +Separate the training set (subsets 0-6). +``` +X_train = dataset[dataset.subset <= 4].copy().sort_values(by = 'usrds_id') +``` +Separate the labels. +``` +y_train = np.array(X_train.pop('died_in_90')) +``` +Separate the validation set (subsets 5-6). This validation set gets used as the 'test' set of data while the model is being trained to avoid ever 'looking' at the test set until we evaluate the model. +``` +X_val = dataset[(dataset.subset == 6) | (dataset.subset == 5)].copy().sort_values(by = 'usrds_id') +``` +Separate labels. +``` +y_val = np.array(X_val.pop('died_in_90')) +``` +Separate the test set (subsets 7 8 9) for evaluating the model to see how well it performs on new data. Sorting by usrds_id is important so that we can calculate the fairness (or you could just run the predictions again and save the order). +``` +X_test = dataset[dataset.subset > 6].copy().sort_values(by = 'usrds_id') +``` +Separate labels. +``` +y_test = np.array(X_test.pop('died_in_90')) +``` +Scale only the numeric columns with an sklearn library **StandardScaler** by fitting the scaler model on the training set, and use this model and transform on the validation and test sets. Then drop the non-feature columns and save as an array. +``` +scaler = StandardScaler() +X_train[nu_cols] = scaler.fit_transform(X_train[nu_cols]) +X_train = np.array(X_train.drop(columns=['subset','usrds_id','impnum'])) + +X_val[nu_cols] = scaler.transform(X_val[nu_cols]) +X_val = np.array(X_val.drop(columns=['subset','usrds_id','impnum'])) + +X_test[nu_cols] = scaler.transform(X_test[nu_cols]) +X_test = np.array(X_test.drop(columns=['subset','usrds_id','impnum'])) +``` + + +Step 3. Build final model + +Build the final model based on the best hyperparameters from the previous cross-validation step. +First set the metric(s) to report. +``` +METRICS = [ + tf.keras.metrics.TruePositives(name='tp'), + tf.keras.metrics.FalsePositives(name='fp'), + tf.keras.metrics.TrueNegatives(name='tn'), + tf.keras.metrics.FalseNegatives(name='fn'), + tf.keras.metrics.BinaryAccuracy(name='accuracy'), + tf.keras.metrics.Precision(name='precision'), + tf.keras.metrics.Recall(name='recall'), + tf.keras.metrics.AUC(name='auc'), + tf.keras.metrics.AUC(name='auc_pr', + num_thresholds=200, + curve="PR", + summation_method="interpolation", + dtype=None, + thresholds=None, + multi_label=False, + label_weights=None) +] +``` +Define a function to create the model layers and complie the model. Insert the best hyperparameters from the cross validation tuning. For details on each line, see the above explanation of the **build_mlp.py** script. +``` +def final_build_mlp( + layers=2, + neurons=16, + output_bias=None, + optimizer='Adam', + activation='relu', + learn_rate=.0002, + dropout_rate=0.2, + kernel_regularizer='l2', + metrics=METRICS +): + + model = tf.keras.Sequential() + + #add 2 dense layers + for i in range(layers): + model.add(Dense( + neurons, + activation=activation, + input_shape=(294,), + kernel_regularizer=kernel_regularizer) + ) + model.add(Dropout(dropout_rate)) + model.add(Dense( + 1, + activation='sigmoid', + bias_initializer=output_bias)) + opt = Adam(lr=learn_rate) + model.compile( + optimizer=opt, + loss=tf.keras.losses.BinaryCrossentropy(), + metrics=metrics) + + return model +``` +Step 4. Instantiate the final model +``` +final_model = final_build_mlp() +``` +Step 5. Train (fit) the final model + +Set the optimal parameters for fitting the model based on results from the cross-validation. +``` +class_weight_10 = {0: 1, 1: 10} +epochs_final = 10 +batches = 256 +``` +Fit the model to the training and validation data. +``` +final_history = final_model.fit( + X_train, + y_train, + batch_size=batches, + epochs=epochs_final, + validation_data=(X_val, y_val), + class_weight=class_weight_10) +``` +Step 6. Get predictions on the training set from the model +``` +train_predictions_final = final_model.predict( + X_train, + batch_size=batches + ) +``` +Step 7. Evaluate the final model + +Evaluate the final model that has been trained on the new data (test set). +``` +test_predictions_final = final_model.predict( + X_test, + batch_size=batches + ) +final_eval = final_model.evaluate( + X_test, + y_test, + batch_size=batches, + verbose=1 + ) +``` +Print results of test set. +``` + res = {} + for name, value in zip(final_model.metrics_names, final_eval): + print(name, ': ', value) + res = {name : value} +``` +***Points to consider*** + +Standardization and scaling of numeric features allows for comparison of multiple features in different units. The model will learn the importance of features better and faster when it isn't overwhelmed by a feature with a much larger range than the others. Scaling and standardization should be done separately for training and test sets so that we are not looking at the test set. + +Neural network models are computationally expensive and there are many parameters to tune and decisions to make. It is beneficial to consult with other experts and test the code before running the full set of data. Multiple CPUs or GPUs can speed up computation time for the cross validation hyperparameter tuning step. + +### 6.3.4.7 Pool Results + +***Steps for running 4_pool_results_from_imputations.ipynb*** + +- Input: +``` +medexpreesrd +2021_MLP_final_ytest_all.pickle +2021_MLP_final_ypred_all.pickle +``` + +- Output: +``` +2021_final_MLP_model_test_pred_proba_pooled.pickle +``` +Step 1. Import libraries +``` +import pickle +import numpy as np +import pandas as pd + +import psycopg2 +import sqlalchemy +from sqlalchemy import create_engine +``` +Step 2. Import results from the MLP model +``` +with open('./results/2021_MLP_final_ytest_all.pickle', 'rb') as f: + y_true_mlp = pickle.load(f) + +with open('./results/2021_MLP_final_ypred_all.pickle', 'rb') as picklefile: + y_pred_proba_mlp = pickle.load(picklefile) +``` +Step 3. Reformat into a pandas dataframe. This makes the data easier to work with. + +The index for the values is zero here b/c we only saved it for the positive class, otherwise it would be 1 +``` +pooled = pd.DataFrame() +pooled['imp1']=y_pred_proba_mlp[0][:,0] +pooled['imp2']=y_pred_proba_mlp[1][:,0] +pooled['imp3']=y_pred_proba_mlp[2][:,0] +pooled['imp4']=y_pred_proba_mlp[3][:,0] +pooled['imp5']=y_pred_proba_mlp[4][:,0] +``` +Step 4. Calculate the mean and standard deviation of the predicted probability for the positive class (died_in_90) for each patient/row. +``` +pooled['score'] = pooled.mean(axis=1) +pooled['std_'] = pooled.std(axis=1) +``` +Step 5. Import the details from the original data + +Enter your credentials for your postgres database. +``` +con = create_engine('postgresql://username:password@location/dbname') + +dataset = pd.read_sql_query('''SELECT usrds_id, died_in_90, subset FROM medxpreesrd;''', con) +``` +Step 6. Sort the values so they are in the same order as when the MLP model was calculated and keep only the test set. +``` +dataset = dataset[dataset.subset > 6].copy().sort_values(by = 'usrds_id').reset_index(drop=True) +``` +Step 7. Merge the details with the pooled predictions. +``` +pooled = pooled.merge(dataset, left_index=True, right_index=True) +``` +Step 8. Save +``` +with open('./results/2021_final_MLP_model_test_pred_proba_pooled.pickle', 'wb') as picklefile: + pickle.dump(pooled, picklefile) +``` + +### 6.3.4.8 Plot Results + +***Steps for running the 5_plot_mlp_roc_auc.ipynb script*** + +- Input: +``` +2021_final_MLP_model_test_pred_proba_pooled.pickle +``` +- Output: +``` +mlp_roc_auc_bw.png +2021_mlp_confusion_matrix.csv +``` + +Step 1. Import libraries +``` +import pandas as pd +import numpy as np +import pickle + +import sys +#path to the functions directory +sys.path.append('../onc_functions/') + +#import custom plotting functions +from plot_functions import onc_calc_cm, onc_plot_roc, onc_plot_precision_recall +``` +Step 2. Load results from the pooled model results +``` +with open('./results/2021_final_MLP_model_test_pred_proba_pooled.pickle', 'rb') as f: + results = pickle.load(f) + +results = results.loc[:,['score','died_in_90','subset','usrds_id']] +results = results.rename(columns={'died_in_90':'y'}) +``` +Step 3. Plot the ROC AUC. This function **onc_plot_roc** is located and imported from /onc_functions/plot_functions.py +``` +def onc_plot_roc(y_true, y_pred, model_name, **kwargs): + ''' + Plot the ROC AUC and return the test ROC AUC results. + INPUT: y_true, y_pred, model_name, **kwargs + ''' + + #calc values for plot + false_positives, true_positives, threshold = roc_curve(y_true, y_pred) + c_roc_auc_score = auc(false_positives, true_positives) + + #set figure params + fig1 = plt.figure(1, figsize=(12,30),dpi=400) + ax1 = plt.subplot2grid((7, 1), (0, 0), rowspan=2) + + #plot reference line for chance + ax1.plot([0, 1], [0, 1], linestyle='--', lw=2, color='gray', + label='Chance', alpha=.8) + + # plot AUC ROC + ax1.plot(false_positives, true_positives, + label=r'ROC (AUC = %0.3f)' % (c_roc_auc_score), + lw=2, alpha=.8, color = 'k') + + # additional figure params + ax1.set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],) + ax1.legend(loc="lower right") + plt.xlabel('1-Specificity') + plt.ylabel('Sensitivity') + plt.rc('axes', labelsize=22) # fontsize of the x and y labels + plt.rc('xtick', labelsize=15) # fontsize of the tick labels + plt.rc('ytick', labelsize=15) # fontsize of the tick labels + plt.rc('legend', fontsize=20) # legend fontsize + # save plot + plt.savefig(model_name + "_roc_auc_bw.png", dpi=400, transparent=True) + plt.show() +``` +``` +onc_plot_roc( + y_true=results.y, + y_pred=results.score, + model_name='mlp'); +``` +![mlp_roc_auc_bw_nothreshold_hires_crop.png](./images/mlp_roc_auc_bw_nothreshold_hires_crop.png) + +Step 4. Print and save the performance metrics at multiple thresholds +``` +def onc_calc_cm(y_true, y_predictions, range_probas=[0.1,0.5]): + ''' + Plot the confusion matrix and scores for multiple thresholds + ''' + df = pd.DataFrame(index = range_probas, + columns=['threshold','sensitivity','specificity', + 'likelihood_ratio_neg','likelihood_ratio_pos', + 'tp','fp','tn','fn','total_survived','total_deceased',]) + for proba_threshold in range_probas: + + cm = confusion_matrix(y_true, y_predictions > proba_threshold) + tn = cm[0][0] + fp = cm[0][1] + + sensitivity = recall_score(y_true, y_predictions > proba_threshold) + specificity = tn / (tn + fp) + + df.loc[proba_threshold, "threshold"] = proba_threshold + df.loc[proba_threshold,"sensitivity"] = sensitivity + df.loc[proba_threshold, "specificity"] = specificity + df.loc[proba_threshold, "likelihood_ratio_neg"] = (1-sensitivity)/specificity + df.loc[proba_threshold, "likelihood_ratio_pos"] = sensitivity/(1-specificity) + df.loc[proba_threshold, "tp"] = cm[1][1] + df.loc[proba_threshold, "fp"] = fp + df.loc[proba_threshold, "tn"] = tn + df.loc[proba_threshold, "fn"] = cm[1][0] + df.loc[proba_threshold, "total_survived"] = np.sum(cm[0]) + df.loc[proba_threshold, "total_deceased"] = np.sum(cm[1]) + return df +``` +``` +cm = onc_calc_cm( + results.y, + results.score, + range_probas=[.10,.20, .30, .40, .50]) +cm.to_csv('./results/2021_mlp_confusion_matrix.csv') +cm +``` +![mlp_cm_orig.png](./images/mlp_cm_orig.png) + +### 6.3.4.9 Fairness assessment +ML models can perform differently for different categories of patients, so the MLP model was assessed for fairness, or how well the model performs for each category of interest (demographics—sex, race, and age—as well as initial dialysis modality). Age is binned into the following categories based on UCSF clinician input and an example in literature: 18-25, 26-35, 36-45, 46-55, 56-65, 66-75, 76-85, 86+. The USRDS predefined categories for race, sex, and dialysis modality were used for the fairness assessment. + +***Steps to running the 6_mlp_fairness_assessment.ipynb script*** + +This script calculates the ROC AUC for specific groups of patients to assess the fairness of the final model. + +- Input: +``` +medexpreesrd +2021_final_MLP_model_test_pred_proba_pooled.pickle +``` +- Output: +``` +complete_fair1.pickle +2021_mlp_fairness.csv +``` +Step 1. Import libraries +``` +import numpy as np +import pandas as pd +import pickle +import sys +#path to the functions directory +sys.path.append('../onc_functions/') + +import psycopg2 +from sqlalchemy import create_engine + +from fairness import get_fairness_assessment +``` + +Step 2. Get the columns of data required to compute fairness assessment and save them +``` +con = create_engine('postgresql://username:password@location/dbname') +df = pd.read_sql_query('''SELECT usrds_id, died_in_90, inc_age, sex, dialtyp, race, hispanic, subset FROM medxpreesrd;''', con) +``` +Step 3. Save +``` +with open('complete_fair1.pickle', 'wb') as picklefile: + pickle.dump(df, picklefile) +``` +Step 4. Import the pooled results from the LR model +``` +with open('./results/2021_final_MLP_model_test_pred_proba_pooled.pickle', 'rb') as f: + proba = pickle.load(f) +``` +Step 5. Merge the fairness details with the results +``` +data = df.merge(proba, on=['usrds_id','subset','died_in_90']) +``` +Step 6. Calculate fairness. The function **get_fairness_assessment** is imported from the /onc_functions/fairness.py file. This function calculates AUC and the confusion matrix from the model prediction scores for specific groups. +``` +def get_fairness_assessment(data, + y_proba_col_name, + y_true_col_name): + + #turn the continuous age variable into age categories + df['agegroup'] = pd.cut(df.inc_age, + bins=[17, 25, 35, 45, 55, 65, 75, 85, 90], + labels=[1, 2, 3, 4, 5, 6, 7, 8]) + + df = df.drop(columns=['inc_age']) + + #replace NaNs with a large number that does not appear in the data, effectively creating another category for missing values + df.loc[:,['race','dialtyp','hispanic']] = df.loc[:,['race','dialtyp','hispanic']].fillna(100.0, axis=1).copy() + + #Identify the cols for the fairness assessment + fairness_cols = ['agegroup', 'sex','dialtyp', 'race','hispanic'] + + #loop through all categories and values to get counts, auc, and confusion matrix + rows_list = [] + for col in fairness_cols: + for name, c in df.groupby(col): + fairness_dict = {} + fairness_dict['Feature'] = col + fairness_dict['Value'] = name + fairness_dict['Count'] = c.shape[0] + + fairness_dict['AUC'] = roc_auc_score(c[y_true_col_name], c[y_proba_col_name]) + tn, fp, fn, tp = confusion_matrix(y_true = c[y_true_col_name], + y_pred = np.where(c[y_proba_col_name] >= 0.5, 1, 0)).ravel() + fairness_dict['TN'] = tn + fairness_dict['FP'] = fp + fairness_dict['FN'] = fn + fairness_dict['TP'] = tp + rows_list.append(fairness_dict) + + #convert results from a list to a dataframe + df_fairness = pd.DataFrame(rows_list) + return df_fairness +``` +Step 7. Calculate the assessment and save the results. +``` +fairness = get_fairness_assessment(data, + y_proba_col_name='score', + y_true_col_name='died_in_90' +) +fairness.to_csv('./results/2021_mlp_fairness.csv') +``` +![mlp_fair.png](./images/mlp_fair.png) + +***Points to consider*** + +Performing the fairness assessment on the categories of interest gives additional insight into how the model performs by different patient categories of interest (by demographics, etc.). Future researchers should perform fairness assessments to better evaluate model performance, especially for models that may be deployed in a clinical setting. Other methods of assessing fairness include evaluating true positives, sensitivity, positive predictive value, etc. at various threshold across the different groups of interest, which would allow selection of a threshold that balances model performance across the groups of interest. + +### 6.3.4.10 Risk assessment + +***Steps for running the 7_mlp_risk.ipynb script*** + +- Input: +``` +complete_fair1.pickle +2021_final_MLP_model_test_pred_proba_pooled.pickle +``` +- Output: +``` +2021_mlp_risk_cat.csv +``` +Step 1. Import libraries +``` +import numpy as np +import pandas as pd +import pickle + +import sys +#path to the functions directory +sys.path.append('../onc_functions/') + +from risk import get_risk_categories + +print('python-' + sys.version) +import datetime +dte = datetime.datetime.now() +dte = dte.strftime("%Y%m%d") +``` + +Step 2. Import the details from the fairness assessment +``` +with open('complete_fair1.pickle','rb') as f: + details = pickle.load(f) +``` + +Step 3. Import the pooled results from the model +``` +with open('./results/2021_final_MLP_model_test_pred_proba_pooled.pickle', 'rb') as f: + y_pred = pickle.load(f) +``` +Step 4. Merge the details with the results +``` +data = df.merge(y_pred, on=['usrds_id','died_in_90','subset']) +``` + +Step 5. Calculate risk. The function **get_risk_categories** is imported from the /onc_functions/risk.py file. + +``` +def get_risk_categories(dataset, y_proba_col_name, y_true_col_name): + + test_x_pd = dataset[dataset.subset > 6].copy().sort_values(by = 'usrds_id') + del dataset + + df = test_x_pd.loc[:,[y_true_col_name,y_proba_col_name]] + + #construct the risk categories from the predicted score + df['risk_categories'] = pd.cut(df[y_proba_col_name], + bins=[-0.1, 0.09, 0.19, 0.29, 0.39, 0.49, 0.59, 0.69, 0.79, 0.89, 0.99], + labels=['0-0.09', '0.1-0.19', '0.2-0.29', '0.3-0.39', '0.4-0.49', + '0.5-0.59','0.6-0.69','0.7-0.79','0.8-0.89','0.9-0.99']) + + #loop through all the categories to get the predicted score + risk_list = [] + for name, c in df.groupby('risk_categories'): + risk_dict = {} + risk_dict['Risk Category'] = name + risk_dict['Count'] = c[y_true_col_name].shape[0] + risk_dict['Count Died in 90'] = c[y_true_col_name].sum() + risk_dict['Count Survived'] = c[y_true_col_name].shape[0]-c[y_true_col_name].sum() + risk_dict['Percent Died in 90'] = c[y_true_col_name].sum()/c[y_true_col_name].shape[0] + + risk_list.append(risk_dict) + + df_risk = pd.DataFrame(risk_list) + return df_risk +``` +Run the function above +``` +risk_cat = get_risk_categories(data, + y_proba_col_name='score', + y_true_col_name='died_in_90') +``` +Step 6. 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