A fuzzy logic-based computational recognition-primed decision model

  • Authors:

  • Yanqing Ji;R. Michael Massanari;Joel Ager;John Yen;Richard E. Miller;Hao Ying

  • Affiliations:

  • Department of Electrical and Computer Engineering, Wayne State University, Room 3144, Engineering Building, 5050 Anthony Wayne Drive, Detroit, MI 48202, USA;Department of Internal Medicine and Center for Healthcare Effectiveness Research, Wayne State University, Detroit, MI, USA;Department of Family Medicine and Public Health Sciences and Division of Biostatistics and Epidemiology, Wayne State University, Detroit, MI, USA;College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, USA;Veterans Affairs Medical Center, Detroit, MI, USA;Department of Electrical and Computer Engineering, Wayne State University, Room 3144, Engineering Building, 5050 Anthony Wayne Drive, Detroit, MI 48202, USA

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2007

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Abstract

The recognition-primed decision (RPD) model is a primary naturalistic decision-making approach which seeks to explicitly recognize how human decision makers handle complex tasks and environment based on their experience. Motivated by the need for quantitative computer modeling and simulation of human decision processes in various application domains, including medicine, we have developed a general-purpose computational fuzzy RPD model that utilizes fuzzy sets, fuzzy rules, and fuzzy reasoning to represent, interpret, and compute imprecise and subjective information in every aspect of the model. Experiences acquired by solicitation with experts are stored in experience knowledge bases. New local and global similarity measures have been developed to identify the experience that is most applicable to the current situation in a specific decision-making context. Furthermore, an action evaluation strategy has been developed to select the workable course of action. The proposed fuzzy RPD model has been preliminarily validated by using it to calculate the extent of causality between a drug (Cisapride, withdrawn by the FDA from the market in 2000) and some of its adverse effects for 100 hypothetical patients. The simulated patients were created based on the profiles of over 1000 actual patients treated with the drug at our medical center before its withdrawal. The model validity was demonstrated by comparing the decisions made by the proposed model and those by two independent internists. The levels of agreement were established by the weighted Kappa statistic and the results suggested good to excellent agreement.