Paper: Multiply sectioned Bayesian networks for neuromuscular diagnosis

Authors:
Yang Xiang;B. Pant;A. Eisen;M. P. Beddoes;D. Poole
Affiliations:
Department of Computer Science, University of Regina, Regina, Sask., S4S 0A2 Canada;Neuromuscular Disease Unit, Vancouver General Hospital Canada;Neuromuscular Disease Unit, Vancouver General Hospital Canada;Department of Electrical Engineering, University of British Columbia Canada;Department of Computer Science, University of British Columbia Canada and Canadian Institute for Advanced Research Canada
Venue:
Artificial Intelligence in Medicine
Year:
1993

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Abstract

A prototype neuromuscular diagnostic system (PAINULIM) that diagnoses painful or impaired upper limbs has been developed based on Bayesian networks. This paper presents nonmathematically the major knowledge representation issues that arose in the development of PAINULIM. Motivated by the computational overhead of large application domains, and the desire to provide a user with an interface that gives a focused display of a subdomain of current interest, we built PAINULIM using the idea of multiply sectioned Bayesian networks. A preliminary evaluation of PAINULIM with 76 patients has demonstrated good clinical performance.