Medical Decision Making

  • Bruce I. Blum


Chapter 2 introduced the concepts of data, information, and knowledge-oriented systems. It was suggested that knowledge applications were an area of research and that the technology required to support the data and information applications existed and was well understood. This view is summarized in Table 9.1. It suggests that for data and information applications, advances will be made by identifying medical and health care problems and engineering the computer systems to implement solutions. The research will emphasize biomedicine and health care; implementation will be primarily an engineering activity. Knowledge- oriented applications, however, are still topics of active research. The paradigms vary from the rigorous application of mathematic principles to the use of artificial intelligence (AI) techniques. This diversity of approaches is not without emotion, hyperbole, or controversy. Yet, as will be shown, this emphasis on medical knowledge processing identifies a broad research area that addresses common problems using different methodologies. From this activity, a science of medical informatics should emerge.


Expert System Medical Decision Medical Knowledge Pleural Fluid Production Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ledley, R. S. and L. B. Lusted, Reasoning Foundations of Medical Diagnosis, Science, 130, July 1959, p. 9.PubMedCrossRefGoogle Scholar
  2. 2.
    Ibid, p. 20.Google Scholar
  3. 3.
    Feinstein, A. R., Clinical Biostatistics XXXIX. The Haze of Bayes, the Aerial Palaces of Decision Analysis, and the Computerized Ouija Board, Clinical Pharmacology and Therapeutics, 21 (4) 1977 p. 495.Google Scholar
  4. 4.
    Elstein, A. S., L. S. Shulman, S. A. Sparafka, et al. Medical Problem Solving, Harvard University Press, 1978.Google Scholar
  5. 5.
    Ibid, p. 281.Google Scholar
  6. 6.
    Ibid, p. 247-8.Google Scholar
  7. 7.
    Grimm, R. H., et al., Evaluation of Patient-Care Protocol Use by Various Providers, NEJ Med. 292: 1975, p. 507.CrossRefGoogle Scholar
  8. 8.
    Bleich, H. L., Computer Evaluation of Acid-Base Disorders, J. Clin. Invest., 48 1969, p. 1690.CrossRefGoogle Scholar
  9. 9.
    Warner, H., Computer-Assisted Medical Decision-Making, Academic Press, 1979, p. 137ff. Examples from HELP which follow are taken from this book and the chapter on HELP in B. I. Blum (ed) Information Systems in Patient Care, pp. 109–128.Google Scholar
  10. 10.
    Faught, E., S. Trader and G. Hanna, Cerebral Complications of Angiography for Transient Ischemia and Stroke-Prediction of Risk, Neurology 29,; 1979, 4–15.PubMedGoogle Scholar
  11. 11.
    Turing, A. M., Intelligent Machinery (1947), in B. Meitzer and D. Miochie (eds), Machine Intelligence 5, Halstead Press, New York, NY 1970.Google Scholar
  12. 12.
    Buchanan, B. G. and E. H. Shortliffe, Rute Based Expert Systems, The MYCIN Experiments of the Stanford Heuristic Programming Project, Addison-Wesley, 1984, p573.Google Scholar
  13. 13.
    Buchanan, op. cit. p. 573.Google Scholar
  14. 14.
    Ibid, p. 599.Google Scholar
  15. 15.
    Aikins, J. S., J. C. Kunz, E. H. Shortliffe, and R. J. Fallat, PUFF: An Expert System for Interpretation of Pulmonary Function Data, Comp. Biomed. Res. 16, 1983, 199–208, p. 203.Google Scholar
  16. 16.
    Langlotz, C. P. and E. H. Shortliffe, Adapting a Consultation System to Critique User Plans, In. J. Man-Machine Studies 19, (1983) 479–496, p. 483.CrossRefGoogle Scholar
  17. 17.
    Miller, A. M., H. E. Pople, Jr., J. D. Myers, INTERNIST-I, An Experimental Computer-Based Diagnostic Consultant for General Internal Medicine, NE J Med., 307: 468–476, 1982, p. 473. The extract of the Sample Manifestation List was also taken from this paper.CrossRefGoogle Scholar
  18. 18.
    Figures 12 and 13 plus portions of the example are taken from H. E. Pople, Jr., Heuristic Methods for Imposing Structure on Ill-Structured Problems: the Structuring of Medical Diagnosis, in P. Szolovits (ed), Artificial Intelligence in Medicine, West- view Press, 1982, pp. 119–190.Google Scholar
  19. 19.
    Kingsland ID, L. C., G. C. Sharp, D. R. Kay, S. M. Weiss, G. C. Roeseler, and D. A. B. Lindberg, An Expert Consultant System in Rheumatology: AI/RHEUM, Sixth Annual Symposium on Computer Applications in Medical Care, 1982, p 748–752.Google Scholar
  20. 20.
    Miller, P. L., Critiquing Anesthetic Management: the ATTENDING Computer System, Anesthesiology, 58: 362–369, 1983.PubMedCrossRefGoogle Scholar
  21. 21.
    Bleich, H. L., Computer Evaluation of Acid-Base Disorders, J. Clin. Invest. 48: 1689–1695, 1969, p. 1694.PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York Inc. 1986

Authors and Affiliations

  • Bruce I. Blum
    • 1
  1. 1.Applied Physics LaboratoryThe Johns Hopkins UniversityLaurelUSA

Personalised recommendations