Management of uncertainty in medicine

Paul R. Cohen, David Day, Jeff De Lisio, Michael Greenberg, Rick Kjeldsen, Dan Suthers, Paul Berman

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

This article discusses MUM, a knowledge-based consultation system designed to manage the uncertainty inherent in medical diagnosis. The primary task of the system is to plan which questions, tests, and treatments to order at each point in a consultation, given current uncertain knowledge about the patient's disease. Managing uncertainty means planning what to do when uncertain; the authors suggest that this ability must be designed in, not added on, to the architectures of knowledge-based systems. MUM is based on one such architecture, implemented as a generalized inference network and planner. The network facilitates local combination of evidence; the planner "reads" the state of the network after each piece of evidence integrated, then decides which evidence to seek on the basis of its several goals.

Original languageEnglish (US)
Pages (from-to)103-116
Number of pages14
JournalInternational Journal of Approximate Reasoning
Volume1
Issue number1
DOIs
StatePublished - Jan 1987
Externally publishedYes

Keywords

  • automated reasoning
  • control
  • diagnosis
  • planning
  • reasoning about uncertainty

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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