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


    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
    Issue number1
    StatePublished - Jan 1987


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

    ASJC Scopus subject areas

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


    Dive into the research topics of 'Management of uncertainty in medicine'. Together they form a unique fingerprint.

    Cite this