Abstract #301082

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JSM 2003 Abstract #301082
Activity Number: 417
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Teaching of Statistics in the Health Sciences
Abstract - #301082
Title: A Bayesian Bridge Between Probability and Fuzziness
Author(s): Patricia Giurgescu*+
Companies: Pace University
Address: Math Dept., Pleasantville, NY, 10570-2700,
Keywords: Bayes theorem ; evidence theory ; conditional fuzzy measure
Abstract:

Great progress has been made in building computer-assisted medical diagnosis systems incorporating fuzziness (as uncertainty associated with several well-defined alternatives or lack of sharp distinguishing boundaries), to ensure high classification performance and reliable confidence measures of diagnosis output. The paper presents an extension of Bayes theorem, in the setting of fuzzy measures. Effective decision making under uncertainty in the health care arena is of growing importance, due to the increasing volume of information available, from new medical technologies and individualized, context-sensitive patient data, often based on imprecise, subjective judgment. In its original formulation, Bayesian conditioning requires new evidence to be expressible as certainty, which is not always a realistic assumption. Applying the Bayesian paradigm to medical diagnosis is more suitable when fuzzy conditional measures are employed, in place of the classical posterior probability P(Di | X) of identifying a disease Di, after processing the diagnostically relevant information X.

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