title: Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. II. Evaluation of diagnostic performance.
abstract: We have developed a probabilistic reformulation of the Quick Medical Reference (QMR) system. In Part I of this two-part series, we described a two-level, multiply connected belief-network representation of the QMR knowledge base and a simulation algorithm to perform probabilistic inference on the reformulated knowledge base. In Part II of this series, we report on an evaluation of the probabilistic QMR, in which we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.
authors.affiliation: Section on Medical Informatics, Stanford University, CA.
authors.forename: B
authors.initials: B
authors.lastname: Middleton
authors.forename: M A
authors.initials: MA
authors.lastname: Shwe
authors.forename: D E
authors.initials: DE
authors.lastname: Heckerman
authors.forename: M
authors.initials: M
authors.lastname: Henrion
authors.forename: E J
authors.initials: EJ
authors.lastname: Horvitz
authors.forename: H P
authors.initials: HP
authors.lastname: Lehmann
authors.forename: G F
authors.initials: GF
authors.lastname: Cooper
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D008838:Microcomputers; D015233:Models, Statistical; D011336:Probability; D012680:Sensitivity and Specificity; D012984:Software; D013269:Stochastic Processes