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Dynamic decision making in stochastic partially observable medical domains: Ischemic heart disease example

  • Milos Hauskrecht
Probabilistic Models and Fuzzy Logic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1211)

Abstract

The focus of this paper is the framework of partially observable Markov decision processes (POMDPs) and its role in modeling and solving complex dynamic decision problems in stochastic and partially observable medical domains. The paper summarizes some of the basic features of the POMDP framework and explores its potential in solving the problem of the management of the patient with chronic ischemic heart disease.

Keywords

Ischemic Heart Disease Markov Decision Process Finite Horizon Partially Observable Markov Decision Process Dynamic Decision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Milos Hauskrecht
    • 1
  1. 1.MIT Lab for Computer ScienceCambridge

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