Uncertainty and decisions in medical informatics.
This paper presents a tutorial introduction to the handling of uncertainty and decision-making in medical reasoning systems. It focuses on the central role of uncertainty in all of medicine and identifies the major themes that arise in research papers. It then reviews simple Bayesian formulations of the problem and pursues the generalization to the Bayesian network methods that are popular today. Decision making is presented from the decision analysis viewpoint, with brief mention of recently-developed methods. The paper concludes with review of more abstract characterization of uncertainty, and anticipates the growing importance of analytic and "data mining" techniques as growing amounts of clinical data become widely available.
Szolovits
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Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge, USA.
D001499:Bayes Theorem; D003657:Decision Making; D003661:Decision Support Techniques; D003663:Decision Trees; D006801:Humans; D008490:Medical Informatics; D008962:Models, Theoretical