A Representation of Uncertainty to Aid Insight into Decision Models

TL;DR

This paper introduces a graphical and computational framework for representing uncertainty in decision models, enhancing interpretability and confidence, demonstrated through a Bayesian network for angina diagnosis.

Contribution

It presents a novel uncertainty representation method that integrates probabilistic distributions and utilities, improving model transparency and customization in clinical decision aids.

Findings

Framework enables dynamic importance assessment of variables.

Allows rapid tailoring to patient-specific data.

Enhances understanding and confidence in decision models.

Abstract

Many real world models can be characterized as weak, meaning that there is significant uncertainty in both the data input and inferences. This lack of determinism makes it especially difficult for users of computer decision aids to understand and have confidence in the models. This paper presents a representation for uncertainty and utilities that serves as a framework for graphical summary and computer-generated explanation of decision models. The application described that tests the methodology is a computer decision aid designed to enhance the clinician-patient consultation process for patients with angina (chest pain due to lack of blood flow to the heart muscle). The angina model is represented as a Bayesian decision network. Additionally, the probabilities and utilities are treated as random variables with probability distributions on their range of possible values. The initial distributions represent information on all patients with anginal symptoms, and the approach allows for rapid tailoring to more patientspecific distributions. This framework provides a metric for judging the importance of each variable in the model dynamically.