PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Inference in the promedas medical expert system
Bastian Wemmenhove, Joris Mooij, Wim Wiegerinck, Martijn Leisink, Bert Kappen and J. Neijt
In: AIME 2007, 7-11 July 2007, The netherlands.

Abstract

Abstract. In the current paper, the Promedas model for internal medi- cine, developed by our team, is introduced. The model is based on up-to- date medical knowledge and consists of approximately 2000 diagnoses, 1000 findings and 8600 connections between diagnoses and findings, cov- ering a large part of internal medicine. We show that Belief Propagation (BP) can be successfully applied as approximate inference algorithm in the Promedas network. In some cases, however, we find errors that are too large for this application. We apply a recently developed method that improves the BP results by means of a loop expansion scheme. This method, termed Loop Corrected (LC) BP, is able to improve the marginal probabilities significantly, leaving a remaining error which is acceptable for the purpose of medical diagnosis.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4885
Deposited By:Bert Kappen
Deposited On:24 March 2009