Knowledge Acquisition by Analysis of Verbatim Protocols

  • Benjamin Kuipers
  • Jerome P. Kassirer

Abstract

How does an expert physician reason about the mechanisms of the body? We are exploring the hypothesis that the physician has a cognitive “causal model” of the patient: a description of the mechanisms of the human body and how they influence each other. This causal model, incorporating the expert’s knowledge of anatomy and physiology, can be used to simulate the normal working of the body, its pathological behavior in a diseased state, and the idiosyncracies that characterize a particular patient. The causal model supports the expert performance of the physician by simulating the possible courses of the patient’s disease and treatment, by serving as a coherency criterion on hypotheses about the patient’s state, and by providing a common framework for explanations and discussion among physicians.

Keywords

Albumin Sodium Chloride Nism Librium Boulder 

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References

  1. Chi, M. T. H., Feltovich, P. J., and Glaser, R. (1982). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5 ,121–152.CrossRefGoogle Scholar
  2. Davis, R. (1982). Teiresias: Applications of meta-level knowledge. In R. Davis and D. B. Lenat, Knowledge-based systems in artificial intelligence. New York: McGraw-Hill.Google Scholar
  3. de Dombal, F. T., Leaper, D. J., Staniland, J. R., McCann, A. P., and Horrocks, J. C. (1972). Computer-aided diagnosis of abdominal pain. British Medical Journal ,2, 9–13.PubMedCrossRefGoogle Scholar
  4. de Kleer, J. (1977). Multiple representations of knowledge in a mechanics problem-solver. Proceedings of the Fifth International Joint Conference on Artificial Intelligence.Google Scholar
  5. de Kleer, J. (1979). The origin and resolution of ambiguities in causal arguments. Proceed ings of the Sixth International Joint Conference on Artificial Intelligence.Google Scholar
  6. de Kleer, J., and Brown, J. S. (1984). A qualitative physics based on confluences. Artificial Intelligence, 24 ,7–83.CrossRefGoogle Scholar
  7. Elstein, A. S., Shulman, L. S., and Sprafka, S. A. (1978). Medical problem solving: An analysis of clinical reasoning. Cambridge, Mass.: Harvard University Press.Google Scholar
  8. Ericsson, K. A., and Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87, 215–251.CrossRefGoogle Scholar
  9. Ericsson, K. A., and Simon, H. A. (1984). Protocol analysis. Cambridge, Mass.: M.I.T. Press.Google Scholar
  10. Forbus, K. D. (1981). Qualitative reasoning about physical processes. Proceedings of the Seventh International Joint Conference on Artificial Intelligence.Google Scholar
  11. Forbus, K. D. (1984). Qualitative process theory. Artificial Intelligence, 24 ,85–168.CrossRefGoogle Scholar
  12. Forrester, J. (1969). Urban dynamics. Cambridge, Mass.: M.I.T. Press.Google Scholar
  13. Guyton, A. C., Jones, C. E., and Coleman, T. G. (1973). Circulatory physiology: Cardiac output and its regulation (2nd ed.). Philadelphia: W. B. Saunders.Google Scholar
  14. Kassirer, J. P., and Gorry, G. A. (1978). Clinical problem solving: A behavioral analysis. Annals of Internal Medicine, 89 ,245–255.PubMedGoogle Scholar
  15. Kassirer, J. P., Kuipers, B. J., and Gorry, G. A. (1982). Toward a theory of clinical expertise. American Journal of Medicine, 73 ,251–259.PubMedCrossRefGoogle Scholar
  16. Kuipers, B. J. (1979). On representing commonsense knowledge. In N. V. Findler (Ed.), Associative networks: The representation and use of knowledge by computers. New York: Academic Press.Google Scholar
  17. Kuipers, B. J. (1982). Getting the envisionment right. Proceedings of the National Conference on Artificial Intelligence (AAAI-82).Google Scholar
  18. Kuipers, B. J. (1984). Commonsense reasoning about causality: Deriving behavior from structure. Artificial Intelligence, 24 ,169–204.CrossRefGoogle Scholar
  19. Kuipers, B. J. (1985). The limits of qualitative simulation. Proceedings of the Ninth International Joint Conference on Artificial Intelligence (IJCAI-85).Google Scholar
  20. Kuipers, B. J., and Kassirer, J. P. (1985). Qualitative simulation in medical physiology: A progress report. Cambridge, Mass.: MIT Laboratory for Computer Science TM-280.Google Scholar
  21. Larkin, J., McDermott, J., Simon, D. P., and Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208 ,1335–1342.PubMedCrossRefGoogle Scholar
  22. Newell, A., and Simon, H. A. (1972). Human problem solving. Englewood Cliffs, N.J.: Prentice-Hall.Google Scholar
  23. Nisbett, R. E., and Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84 ,231–259.CrossRefGoogle Scholar
  24. Patil, R. S. (1981). Causal representation of patient illness for electrolyte and acid-base diagnosis. Cambridge, Mass.: MIT Laboratory for Computer Science TR-267.Google Scholar
  25. Pople, H. E., Jr. (1982). Heuristic methods for imposing structure on ill structured problems: The structuring of medical diagnostics. In P. Szolovits (Ed.), Artificial intelligence in medicine. Boulder, Co.: AAAS/Westview Press.Google Scholar
  26. Rimoldi, H. J. A. (1961). The test of diagnostic skills. Journal of Medical Education, 36 ,73.PubMedGoogle Scholar
  27. Steele, G. L., Jr. (1980). The definition and implementation of a computer programming language based on constraints. Cambridge, Mass.: MIT Artificial Intelligence Laboratory TR-595.Google Scholar
  28. Valtin, H. (1973). Renal function: Mechanisms preserving fluid and solute balance in health. Boston: Little, Brown.Google Scholar

Copyright information

© Plenum Press, New York 1987

Authors and Affiliations

  • Benjamin Kuipers
    • 1
  • Jerome P. Kassirer
    • 2
  1. 1.Department of Computer SciencesUniversity of Texas at AustinAustinUSA
  2. 2.Department of MedicineTufts University School of MedicineBostonUSA

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