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Medical decision making using Ignorant Influence Diagrams

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Artificial Intelligence in Medicine (AIME 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 934))

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Abstract

Bayesian Belief Networks (bbns) play a relevant role in the field of Artificial Intelligence in Medicine and they have been successfully applied to a wide variety of medical domains. An appealing character of bbns is that they easily extend into a complete decision-theoretic formalism known as Influence Diagrams (ids). Unfortunately, bbns and ids require a large amount of information that is not always easy to obtain either from human experts or from the statistical analysis of databases. In order to overcome this limitation, we developed a class of ids, called Ignorant Influence Diagrams (iids), able to reason on the basis of incomplete information and to to improve the accuracy of the decisions as a monotonically increasing function of the available information. The aim of this paper is show how iids can be useful to model medical decision making with incomplete information.

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Author information

Authors and Affiliations

  1. Cognitive Studies in Medicine McGill Cognitive Science Centre, McGill University, Montreal, Canada

    Marco Ramoni & Vimla Patel

  2. Laboratorio di Informatica Medica Dipartimento di Informatica e Sistemistica, Università di Pavia, Pavia, Italy

    Alberto Riva & Mario Stefanelli

Authors
  1. Marco Ramoni
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  2. Alberto Riva
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  3. Mario Stefanelli
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  4. Vimla Patel
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Editor information

Pedro Barahona Mario Stefanelli Jeremy Wyatt

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© 1995 Springer-Verlag Berlin Heidelberg

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Ramoni, M., Riva, A., Stefanelli, M., Patel, V. (1995). Medical decision making using Ignorant Influence Diagrams. In: Barahona, P., Stefanelli, M., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1995. Lecture Notes in Computer Science, vol 934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60025-6_132

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  • DOI: https://doi.org/10.1007/3-540-60025-6_132

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60025-1

  • Online ISBN: 978-3-540-49407-2

  • eBook Packages: Springer Book Archive

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