title: Reasoning requirements for diagnosis of heart disease.
abstract: Over the past dozen years, the Heart Disease Program (HDP) has been developed to assist physicians in reasoning about cardiovascular disorders. Driven by several evaluations, the inference mechanism has progressed from a logic based model, to a Bayesian Probability Network (BPN) and finally a pseudo-Bayesian network with temporal and severity reasoning. Though aspects of cardiovascular reasoning are handled well by BPNs, temporal reasoning, homeostatic feedback mechanisms and effects of disease severities require additional inference strategies. This article discusses how these reasoning problems are handled, and deals with closely linked issues in building the user interface to collect detailed cardiovascular data and provide clear explanations of diagnoses.
authors.affiliation: MIT Lab for Computer Science, Cambridge, MA 02139, USA. wjl@mit.edu
authors.forename: W J
authors.initials: WJ
authors.lastname: Long
authors.forename: H
authors.initials: H
authors.lastname: Fraser
authors.forename: S
authors.initials: S
authors.lastname: Naimi
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D003936:Diagnosis, Computer-Assisted; D006331:Heart Diseases / Q000175:diagnosis*; D006801:Humans; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer