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Evidential inference with embedded pattern classifiers: towards a medical expert system for diagnosis
Liu Jiming; Yuen P.C.; Tang Yuan Y.; Wang Xiaoru; Ying Hai
1996-12-01
Source PublicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume2
Pages1096-1101
AbstractIn this paper, we describe a novel quantitative approach to medical diagnosis. Drawing on sound mathematical theories as well as the promising results of previous experiments, the proposed approach provides a computational solution to the modeling and aggregating of partial evidential observations to assure an accurate diagnosis. This approach is particularly useful for diagnosing cases in which a complete set of symptoms is too difficult to observe and the diagnostic judgments are subject to human errors. This paper presents several experiments in which real-world diagnostic problems were investigated. In particular, it attempts to show that (1) with a limited number of case samples, our implication-induction algorithm is capable of inducing implication networks useful for making evidential inferences based on partial observations, (2) observation driven by a network entropy optimization mechanism is effective in reducing the uncertainty of predicted events, and (3) the network-based evidentially predicted events or attributes can provide sufficient information for pattern classification.
URLView the original
Language英語
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Document TypeConference paper
专题University of Macau
AffiliationHong Kong Baptist University
推荐引用方式
GB/T 7714
Liu Jiming,Yuen P.C.,Tang Yuan Y.,et al. Evidential inference with embedded pattern classifiers: towards a medical expert system for diagnosis[C],1996:1096-1101.
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