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 Jan P Neijt
In: Proceedings of the 11th Conference on Artificial Intelligence in Medicine (AIME 07) Lecture Notes in Computer Science , 4594 . (2007) Springer , pp. 456-460. ISBN 978-3-540-73598-4


In the current paper, the Promedas model for internal medicine, 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, covering 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:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:3215
Deposited By:Joris Mooij
Deposited On:20 January 2008