Using Graphical-Belief to Predict Risk for Coronary Artery Disease

Medical knowledge about risks consists of a combination of structural information about known biological facts and probabilistic or actuarial information about exposures to hazards and recovery rates. While both types of information present significant practical challenges, probabilistic information is especially difficult to use because (1) it requires constant maintenance as new studies provide new data and (2) it usually comes in the form of study results which are not ideally suited for making individual predictions. The program GRAPHICAL-BELIEF is an environment for building and manipulating complex risk models. Graphical models can store and manipulate both structural and probabilistic knowledge and the model graph—in which nodes represent variables and edges represent relationships—is a natural visual metaphor for the more complex mathematical model. GRAPHICAL-BELIEF provides tools for both model manipulations and maintaining the knowledge bases on which the model is built. This paper introduces GRAPHICAL-BELIEF through an extended example: a model built from a study of patients with coronary artery disease. It shows how the model can provide valuable information about risk to the patient and value of information for medical tests