title: Multiply sectioned Bayesian networks for neuromuscular diagnosis.
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.
authors.affiliation: Department of Computer Science, University of Regina, Sask., Canada.
authors.forename: Y
authors.initials: Y
authors.lastname: Xiang
authors.forename: B
authors.initials: B
authors.lastname: Pant
authors.forename: A
authors.initials: A
authors.lastname: Eisen
authors.forename: M P
authors.initials: MP
authors.lastname: Beddoes
authors.forename: D
authors.initials: D
authors.lastname: Poole
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D004576:Electromyography; D005069:Evaluation Studies as Topic; D006801:Humans; D015233:Models, Statistical; D009431:Neural Conduction / Q000502:physiology; D009468:Neuromuscular Diseases / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology; D012016:Reference Values; D012984:Software; D016896:Treatment Outcome