Osler

Osler is a Python library for creating differential diagnosis decision trees.

Installation

Use the Setup.py to install osler.

python setup.py install

Underlying Assumptions

This package allows for creating a differential diagnosis decision tree based on the following assumptions:

• It is possible to test every assertion.
• Assertions are part of a propositional logic.

Features

• Given two diagnoses, determines differential criteria.
• Given more than two diagnoses, determines the most efficient decision tree, given constraints.

Features to Add

• Arrive at most likely diagnosis in the least number of steps.

Usage

A minimal example:

from osler.assertion import Assertion
from osler.criterion import Criterion
from osler.diagnosis import Diagnosis
from osler.issue import Issue
from osler.engine import Matrix

#Defining assertions

# Assertion(Proposition)
assertionA = Assertion("assertion A")
assertionB = Assertion("assertion B")
assertionC = Assertion("assertion C")
assertionD = Assertion("assertion D")
assertionE = Assertion("assertion E")
assertionX = Assertion("assertion X")
assertionY = Assertion("assertion Y")

#Defining criteria

# Criterion(Assertion, Truth Value)
criterionA = Criterion(assertionA, True)
criterionB = Criterion(assertionB, True)
criterionC = Criterion(assertionC, True)
criterionD = Criterion(assertionD, True)
criterionE = Criterion(assertionE, True)

criterionNA = Criterion(assertionA, False)
criterionNB = Criterion(assertionB, False)
criterionNC = Criterion(assertionC, False)
criterionND = Criterion(assertionD, False)
criterionNE = Criterion(assertionE, False)

criterionX = Criterion(assertionX, True)
criterionY = Criterion(assertionY, False)

#Defining diagnoses

# Diagnosis(Name, Set of Criteria, Probability)
diagnosis1 = Diagnosis('Diagnosis 1', {criterionA, criterionNB, criterionC, criterionX}, 0.25)
diagnosis2 = Diagnosis('Diagnosis 2', {criterionNA, criterionNC, criterionD, criterionY, criterionX}, 0.15)
diagnosis3 = Diagnosis('Diagnosis 3', {criterionNA, criterionC, criterionX}, 0.5)
diagnosis4 = Diagnosis('Diagnosis 4', {criterionNA, criterionNC, criterionND, criterionE, criterionX}, 0.25)
diagnosis5 = Diagnosis('Diagnosis 5', {criterionNA, criterionNC, criterionND, criterionNE, criterionX}, 0.025)

#Defining an issue

# Issue(Name, Description, Set of Diagnoses)
issue = Issue('Issue I', {diagnosis1, diagnosis2, diagnosis3, diagnosis4, diagnosis5})

#Building a test tree

matrix = Matrix(issue)
matrix.construct_tree()

matrix.to_png("tree.png")
matrix.to_svg("tree.svg")

This results in the decision tree:

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT