A knowledge-based model construction approach to medical decision making.
We present a framework for representing the probabilistic effects of actions and contingent treatment plans. Our language has a well-defined declarative semantics and we have developed an implemented algorithm (named BNG) that generates Bayesian networks (BN) to compute the posterior probabilities of queries. In this paper we address the problem of projecting a contingent treatment plan by automatically constructing a structure of interrelated BNs, which we call a BN-graph, and applying the available propagation procedures on it. To address the optimal plan generation, we base our approach on the observation that normally the target plan space has a well-defined structure. We provide a language to describe plan spaces which resembles a programming language with loops and conditionals. We briefly present the procedures for finding the optimal plan(s) from such specified plan spaces.
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Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee 53201, USA. liem@cs.uwm.edu
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D000208:Acute Disease; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003658:Decision Making, Computer-Assisted; D003661:Decision Support Techniques; D006801:Humans; D016571:Neural Networks, Computer; D012984:Software; D013924:Thrombophlebitis / Q000175:diagnosis / Q000628:therapy