Medical decision making using Ignorant Influence Diagrams

  • Marco Ramoni
  • Alberto Riva
  • Mario Stefanelli
  • Vimla Patel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 934)


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.


Conditional Probability Incomplete Information Expected Utility Stochastic Variable Atomic Proposition 
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 1995

Authors and Affiliations

  • Marco Ramoni
    • 1
  • Alberto Riva
    • 2
  • Mario Stefanelli
    • 2
  • Vimla Patel
    • 1
  1. 1.Cognitive Studies in Medicine McGill Cognitive Science CentreMcGill UniversityMontrealCanada
  2. 2.Laboratorio di Informatica Medica Dipartimento di Informatica e SistemisticaUniversità di PaviaPaviaItaly

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