Management of uncertainty in a medical expert system

  • D. L. Hudson
  • M. E. Cohen
Section III Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 286)


The use of uncertainty in a rule-based expert system for the analysis of chest pain is discussed. The system, EMERGE, has been evaluated retrospectively and prospectively and has been found to perform extremely well. The original system has been altered to handle degrees of presence of symptoms and variable contribution of antecedents. It also utilizes a logical construct which generalizes traditional AND/OR logic.


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  1. [1]
    Anderson, J., Kohout, L.J., Bandler, W., Thagner, C., A knowledge-based clinical decision support system using new techniques: CLINAID, Proc., AAMSI, 187–192, 1985.Google Scholar
  2. [2]
    Ben-Bassat, M., Campell, D.B., et al., Evaluating multimembership classifiers: A methodology and application to the MEDAS diagnostic system, IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-5:2:225–229, 1983.Google Scholar
  3. [3]
    Davis, R., Buchanan, B., Shortliffe, E., Production rules as a representation for a knowledge-based consultation program, Artificial Intelligence, 8:15–45, 1977.Google Scholar
  4. [4]
    Hudson, D.L., Use of certainty factors to determine emergency room priorities, Proc., AAMSI, 240–244, 1983.Google Scholar
  5. [5]
    Hudson, D.L., Cohen, M.E., Development of decision making rules for transportable, microcomputer-based expert systems in medicine, Journal of Clinical Engineering, 9:4:301–312, 1984.Google Scholar
  6. [6]
    Hudson, D.L., Cohen, M.E., The role of user-interface in a medical expert system, Proc., Computer Applications in Medical Care, 9:232–236, 1985.Google Scholar
  7. [7]
    Hudson, D.L., Cohen, M.E., Deedwania, P.C., EMERGE, An expert system for chest pain analysis, Approximate Reasoning in Expert Systems, M. Gupta, A. Kandel, W. Bandler, J. Kiszka, Eds., Elsevier/No. Holland, New York, 1985.Google Scholar
  8. [8]
    Hudson, D.L., Deedwania, P.C., Cohen, M.E., Watson, P.E., Prospective analysis of EMERGE, an expert system for chest pain analysis, Proc., Computers in Cardiology, 19–24, 1984.Google Scholar
  9. [9]
    Hudson, D.L., Estrin, T., Derivation of rule-based knowledge from established medical outlines, Computers in Biology and Medicine, 14:3–13, 1984.Google Scholar
  10. [10]
    Hudson, D., Estrin, T., EMERGE, A data-driven medical decision making aid, IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-6:87–91, 1984.Google Scholar
  11. [11]
    Kulikowski, C.A., Artificial intelligence methods and systems for medical consultation, IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-2, 5:464–476, 1982.Google Scholar
  12. [12]
    Kulikowski, C., Artificial intelligence methods for medical consultations, IEEE Trans. Pattern Analysis and Machine Intelligence, 464–476, 1980.Google Scholar
  13. [13]
    Lewis, C.E., et al., The Atlas, UCLA Experimental Medical Care Review Organization, Regents, University of California, 1976.Google Scholar
  14. [14]
    Miller, P.L., Angers, D., et al., Teaching with "ATTENDING": A practical way to "debug" an expert knowledge base, AAMSI, 87–91, 1983.Google Scholar
  15. [15]
    Miller, P.L., Blumenfrucht, S.J., Black, H.R., An expert system which critiques the workings of pheochromcytoma, AAMSI, 297–300, 1985.Google Scholar
  16. [16]
    Miller, R.A., Pople, H.E., Myers, J.D., INTERNIST-1, An experimental computer-based diagnostic consultant for general internal medicine, New England Journal of Medicine, 307:468–476, 1982.Google Scholar
  17. [17]
    Parker, S.G., Gorry, F.A., Kassirer, J., Schwartz, W., Towards the simulation of clinical cognition: Taking a present illness by computers, American Journal of Medicine, 60:981–996, 1976.Google Scholar
  18. [18]
    Shortliffe, E.H., Computer-based medical consultations, MYCIN, Elsevier/North Holland, New York, 1976.Google Scholar
  19. [19]
    Shortliffe, E.H., Buchanan, B.G., Feigenbaum, E.A., Knowledge engineering for medical decision making: A review of computer-based clinical decision aids, Proc. IEEE, 67:9:1207–1224, 1979.Google Scholar
  20. [20]
    Shortliffe, E.H., Scott, A.C., Eischoff, M.D., et al., ONCOCIN: An expert system for oncology protocol management, Proc. IJCAI-81:876–881, 1981.Google Scholar
  21. [21]
    Szolovitz, P., Pauker, S.G., Categorical and probabilistic reasoning in medical diagnosis, Artificial Intelligence 11:115–144, 1978.Google Scholar
  22. [22]
    United States Department of Health, Report on Artificial Intelligence in Medicine, 1980.Google Scholar
  23. [23]
    Yager, R., General multiple-objective decision functions and liguistically quantified statements, Int. J. Man-Machine Studies, 21:389–400, 1984.Google Scholar
  24. [24]
    Yager, R., Approximate reasoning as a basis for rule-based expert systems, IEEE Tans. on Systems, Man, and Cybernetics, SMC-14, 4:636–643, 1984.Google Scholar
  25. [25]
    Zadeh, L.A., A theory of approximate reasoning, in Machine Intelligence 9, J.E. Hayes, D. Michie, L.I. Kulich, Editors, New York, Wiley, 149–194, 1979.Google Scholar
  26. [26]
    Zadeh, L.A., The role of fuzzy logic in the management of uncertainty in expert systems, in "Approximate Reasoning in Expert Systems", M.M. Gupta, A. Kandel, W. Bandler, J.B. Kiszka, Eds., Elsevier Science Publishing, 3–31, 1985.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • D. L. Hudson
    • 1
  • M. E. Cohen
    • 2
  1. 1.Section on Medical Information ScienceUniversity of CaliforniaSan Francisco
  2. 2.Department of MathematicsCalifornia State UniversityFresno

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