Causal Models for Medical Artificial Intelligence

  • Perry L. Miller
  • Paul R. Fisher
Part of the Computers and Medicine book series (C+M)

Abstract

A large number of expert systems have been developed using artificial intelligence (AI) techniques to assist in medical diagnosis and treatment.1,2 Most of these systems involve “surface models” of their domains rather than “causal models” of the underlying physiologic and pathophysiologic processes. Such a surface model links sets of patient findings with different diseases to assist diagnosis or defines the clinical conditions for which particular treatments are recommended.

Keywords

Cholesterol Filtration Covariance Germinal Glaucoma 

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References

  1. 1.
    Clancey WJ, Shortliffe EH (eds): Readings in Medical Artificial Intelligence: The First Decade. Reading, MA: Addison -Wesley, 1984.Google Scholar
  2. 2.
    Szolovits P (ed): Artificial Intelligence in Medicine. Boulder, CO: Westview Press, 1982.Google Scholar
  3. 3.
    Weiss SM, Kulikowski CA, Amarel S, Safir A: A model-based method for computer-aided medical decision making. Artif Intell 11:145, 1978.CrossRefGoogle Scholar
  4. 4.
    Patil RS, Szolovits P, Schwartz WB: Causal understanding of patient illness in medical diagnosis. p. 893. In: Proceedings of the Seventh International Joint Conference on Artificial Intelligence. Vancouver: 1981.Google Scholar
  5. 5.
    Patil RS: Causal Representation of Patient Illness for Electrolyte and Acid-Base Diagnosis. MIT/LCS/TR-267. Cambridge MA: MIT Laboratory for Computer Science, 1981.Google Scholar
  6. 6.
    Long WJ, Naimi S, Criscitiello MG, Kurzrok S: Reasoning about therapy from a physiologic model. p. 756. In: Proceedings of MEDINFO 86. Washington, DC: 1986.Google Scholar
  7. 7.
    Kuipers B: Qualitative Simulation of Mechanisms. MIT/LCS/TM-274. Cambridge, MA: MIT Laboratory for Computer Science, 1985.Google Scholar
  8. 8.
    Widman LE: Represention method for dynamic causal knowledge using semi-quantitative simulation. p. 180. In: Proceedings of MEDINFO 86. Washington, DC: 1986.Google Scholar
  9. 9.
    Cooper G: NESTOR: A Medical Decision Support System that Integrates Causal, Temporal and Probabilistic Knowledge. PhD thesis. Computer Science, Stanford University, 1984.Google Scholar
  10. 10.
    Blum RL: Computer-assisted design of studies using routine clinical data. Ann Intern Med 104:858, 1986.PubMedGoogle Scholar
  11. 11.
    Swartout WR: Explaining and justifying expert consultation programs. p. 815. In: Proceedings of the Seventh International Joint Conference on Artificial Intelligence. Vancouver: 1981.Google Scholar
  12. 12.
    Rennels GD: A Computational Model of Reasoning from the Clinical Literature. PhD thesis. Medical Information Sciences, Stanford University, 1986.Google Scholar
  13. 13.
    Rennels GD. Shortliffe EH, Stockdale FE, Miller PL: A computational model of reasoning from the clinical literature. Comput Methods Programs Biomed 24:139, 1987.PubMedCrossRefGoogle Scholar
  14. 14.
    Fisher PR, Miller PL, Swett HA: A script-based representation of medical knowledge involving multiple perspectives. p. 233. In: Proceedings of the American Association of Medical Systems and Informatics Congress-87, San Francisco: 1987.Google Scholar

Copyright information

© Springer-Verlag New York Inc. 1988

Authors and Affiliations

  • Perry L. Miller
  • Paul R. Fisher

There are no affiliations available

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