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Knowledge Acquisition and Automated Generation of Bayesian Networks for a Medical Dialogue and Advisory System

  • Joachim Horn
  • Thomas Birkhölzer
  • Oliver Hogl
  • Marco Pellegrino
  • Ruxandra Lupas Scheiterer
  • Kai-Uwe Schmidt
  • Volker Tresp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2101)

Abstract

Probabilistic models such as Bayesian networks [6] are well suited for medical decision support and are the basis of many successful applications [1],[3],[4],[8],[9],[10]. Bayesian networks provide a rigorous and efficient framework for inference, i.e. for calculating the probability of each stochastic variable given a set of observations. However, knowledge acquisition and generation of the network are still demanding tasks when large medical domains have to be modelled.

Keywords

Conditional Probability Probabilistic Model Bayesian Network Knowledge Acquisition Automate Generation 
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 2001

Authors and Affiliations

  • Joachim Horn
    • 1
  • Thomas Birkhölzer
    • 2
  • Oliver Hogl
    • 3
  • Marco Pellegrino
    • 1
  • Ruxandra Lupas Scheiterer
    • 1
  • Kai-Uwe Schmidt
    • 4
  • Volker Tresp
    • 1
  1. 1.Siemens AG, Corporate TechnologyInformation and CommunicationsMunichGermany
  2. 2.Siemens AG, Medical SolutionsSoftware Components and WorkstationsErlangenGermany
  3. 3.Bavarian Research Center for Knowledge-Based SystemsKnowledge Acquisition GroupErlangenGermany
  4. 4.Siemens AG, Medical SolutionsErlangenGermany

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