Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences.

Journal: Artificial intelligence in medicine
Published Date: Jan 1, 2016

Abstract

OBJECTIVES: Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision.

Authors

  • Anthony Costa Constantinou
    Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, Mile End Campus, Computer Science Building, E1 4NS London, UK. Electronic address: anthony@constantinou.info.
  • Barbaros Yet
    Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, Mile End Campus, Computer Science Building, E1 4NS London, UK.
  • Norman Fenton
    Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, Mile End Campus, Computer Science Building, E1 4NS London, UK.
  • Martin Neil
    Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, Mile End Campus, Computer Science Building, E1 4NS London, UK.
  • William Marsh
    Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, Mile End Campus, Computer Science Building, E1 4NS London, UK.

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