Medical decision making using ignorant influence diagrams

Marco Ramoni, Alberto Riva, Mario Stefanelli, Vimla Patel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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 ros 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.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Medicine - 5th Conference on Artificial Intelligence in Medicine Europe, AIME 1995, Proceedings
Number of pages12
StatePublished - 1995
Externally publishedYes
Event5th Conference on Artificial Intelligence in Medicine Europe, AIME 1995 - Pavia, Italy
Duration: Jun 25 1995Jun 28 1995

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume934 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other5th Conference on Artificial Intelligence in Medicine Europe, AIME 1995

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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