Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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.
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.
Davis, R., Buchanan, B., Shortliffe, E., Production rules as a representation for a knowledge-based consultation program, Artificial Intelligence, 8:15–45, 1977.
Hudson, D.L., Use of certainty factors to determine emergency room priorities, Proc., AAMSI, 240–244, 1983.
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.
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.
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.
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.
Hudson, D.L., Estrin, T., Derivation of rule-based knowledge from established medical outlines, Computers in Biology and Medicine, 14:3–13, 1984.
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.
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.
Kulikowski, C., Artificial intelligence methods for medical consultations, IEEE Trans. Pattern Analysis and Machine Intelligence, 464–476, 1980.
Lewis, C.E., et al., The Atlas, UCLA Experimental Medical Care Review Organization, Regents, University of California, 1976.
Miller, P.L., Angers, D., et al., Teaching with "ATTENDING": A practical way to "debug" an expert knowledge base, AAMSI, 87–91, 1983.
Miller, P.L., Blumenfrucht, S.J., Black, H.R., An expert system which critiques the workings of pheochromcytoma, AAMSI, 297–300, 1985.
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.
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.
Shortliffe, E.H., Computer-based medical consultations, MYCIN, Elsevier/North Holland, New York, 1976.
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.
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.
Szolovitz, P., Pauker, S.G., Categorical and probabilistic reasoning in medical diagnosis, Artificial Intelligence 11:115–144, 1978.
United States Department of Health, Report on Artificial Intelligence in Medicine, 1980.
Yager, R., General multiple-objective decision functions and liguistically quantified statements, Int. J. Man-Machine Studies, 21:389–400, 1984.
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.
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.
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.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1987 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hudson, D.L., Cohen, M.E. (1987). Management of uncertainty in a medical expert system. In: Bouchon, B., Yager, R.R. (eds) Uncertainty in Knowledge-Based Systems. IPMU 1986. Lecture Notes in Computer Science, vol 286. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-18579-8_28
Download citation
DOI: https://doi.org/10.1007/3-540-18579-8_28
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-18579-6
Online ISBN: 978-3-540-48020-4
eBook Packages: Springer Book Archive