A Representation of Uncertainty to Aid Insight into Decision Models
This addresses the challenge for clinicians and patients in interpreting uncertain decision aids, though it is incremental as it builds on existing Bayesian methods.
The paper tackles the problem of uncertainty in decision models by presenting a representation for uncertainty and utilities to aid user understanding and confidence, applied to a Bayesian decision network for angina consultations to dynamically assess variable importance.
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.