title: A sequential decision-theoretic model for medical diagnostic system.
abstract: Although diagnostic expert systems using a knowledge base which models decision-making of traditional experts can provide important information to non-experts, they tend to duplicate the errors made by experts. Decision-Theoretic Model (DTM) is therefore very useful in expert system since they prevent experts from incorrect reasoning under uncertainty. For the diagnostic expert system, corresponding DTM and arithmetic are studied and a sequential diagnostic decision-theoretic model based on Bayesian Network is given. In the model, the alternative features are categorized into two classes (including diseases features and test features), then an arithmetic for prior of test is provided. The different features affect other features weights are also discussed. Bayesian Network is adopted to solve uncertainty presentation and propagation. The model can help knowledge engineers model the knowledge involved in sequential diagnosis and decide evidence alternative priority. A practical example of the models is also presented: at any time of the diagnostic process the expert is provided with a dynamically updated list of suggested tests in order to support him in the decision-making problem about which test to execute next. The results show it is better than the traditional diagnostic model which is based on experience.
authors.forename: Aiping
authors.initials: A
authors.lastname: Li
authors.forename: Songchang
authors.initials: S
authors.lastname: Jin
authors.forename: Lumin
authors.initials: L
authors.lastname: Zhang
authors.forename: Yan
authors.initials: Y
authors.lastname: Jia
mesh_terms: D001499:Bayes Theorem; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D006801:Humans; D008962:Models, Theoretical
keywords: Bayesian Network; Diagnostic expert system; decision-theoretic model; sequential diagnosis