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Medical Bayes Networks

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Medical Data Analysis (ISMDA 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1933))

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Abstract

In this paper a succint overview of the Bayesian network paradigm is presented, in an introductory manner. The reader is not sup- posed to have knowledge about it, although some notions of probability must be taken into account. Bayesian networks are used as inference tools in probabilistic expert systems, being its utilization extended to many research and application fields. Some examples in the medical world are presented, as well as the way they can be constructed and used. We do not emphasize in the calculi to be done; as there are many commercial and free software packages, they can be used without deep knowledge about the formulae to be applied.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and Artificial Intelligence, University of the Basque Country, P.O. Box 649, E-20080, San Sebastián, Spain

    Basilio Sierra, Iñaki Inza & Pedro Larrañaga

Authors
  1. Basilio Sierra
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  2. Iñaki Inza
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  3. Pedro Larrañaga
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Editor information

Editors and Affiliations

  1. Fachbereich Biologie und Informatik AG Adaptive Systemarchitektur, Johann Wolfgang Goethe-Universität, Robert-Mayer-Str. 11-15, 60054, Frankfurt a.M., Germany

    Rüdiger W. Brause

  2. Klinik für Allgemein- und Gefässchirurgie, Johann Wolfgang Goethe-Universität, Theodor-Stern-Kai 7, 60590, Frankfurt a.M., Germany

    Ernst Hanisch

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© 2000 Springer-Verlag Berlin Heidelberg

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Sierra, B., Inza, I., Larrañaga, P. (2000). Medical Bayes Networks. In: Brause, R.W., Hanisch, E. (eds) Medical Data Analysis. ISMDA 2000. Lecture Notes in Computer Science, vol 1933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39949-6_2

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  • DOI: https://doi.org/10.1007/3-540-39949-6_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41089-8

  • Online ISBN: 978-3-540-39949-0

  • eBook Packages: Springer Book Archive

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