Answer justification in medical decision support systems based on Bayesian classification.

Abstract

Answer justification refers to the ability of a computer program to explain how or why it arrived at a particular conclusion. This paper presents a new method for automated answer justification that is suitable for use in computer-supported decision aids in medicine which are based on Bayesian classification. The factors most responsible for the relative ordering of posterior probabilities of outcomes are identified by analyzing the prior and conditional probabilities used to generate them. This approach is illustrated using a computer decision aid for stroke classification and is seen to produce understandable and clinically plausible explanations.