Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base

Blackford Middleton, Michael Shwe, David Heckerman, Max Henrion, Eric Horvitz, Harold Lehmann, Gregory Cooper

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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.

Keywords: Expert Systems, computer-aided Diagnosis, probabilistic inference, belief networks

In: B. Middleton, M.A. Shwe, D.E. Heckerman, M. Henrion, E.J.Horvitz, H.P. Lehmann, and G.E. Cooper. Probabilistic Diagnosis Using a Reformulation of the Internist-1/QMR Knowledge Base. II. Evaluation of Diagnostic Performance. Methods of Information in Medicine, Vol. 30, pp. 256-267, 1991. Also, Stanford University Computer Science Department Technical Report KSL-90-68, 1990.

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