An Approximate Reasoning Model for Medical Diagnosis

Part of the Studies in Computational Intelligence book series (SCI, volume 492)


Medical diagnosis is a classical example of approximate reasoning, and also one of the earliest applications of expert systems. The existing approaches to approximate reasoning in medical diagnosis are mainly based on Probability Theory and/or Multivalued Logic. Unfortunately, most of these approaches have not been able to model medical diagnostic reasoning sufficiently, or in a clinically intuitive way. The model described in this paper attempts to overcome the main limitations of the existing approaches.


approximate reasoning medical expert systems inference model psychiatry 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adlassnig, K.P., Kolarzs, G.: Representation and semiautomatic acquisition of medical knowledge in cadlag-1 and cadiag-2. Computers and Biomedical Research 19, 63–79 (1986)CrossRefGoogle Scholar
  2. 2.
    Andreassen, S., Jensen, F.V., Olesen, K.G.: Medical expert systems based on causal probabilistic networks. International Journal of Bio-Medical Computing 28, 1–30 (1991)CrossRefGoogle Scholar
  3. 3.
    Boegl, K., Adlassnig, K.P., Hayashi, Y., Rothenfluh, T.E., Leitich, H.: Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system. Artificial Intelligence in Medicine 30, 1–26 (2004)CrossRefGoogle Scholar
  4. 4.
    Chard, T., Rubenstein, E.M.: A model-based system to determine the relative value of different variables in a diagnostic system using bayes theorem. International Journal of Bio-Medical Computing 24, 133–142 (1989)CrossRefGoogle Scholar
  5. 5.
    Cohen, L.J.: Applications of Inductive Logic. Oxford University Press, Clarendon (1980)Google Scholar
  6. 6.
    Dempster, A.: Upper and Lower Probabilities Induced by a Multivalued Mapping. In: Yager, R.R., Liu, L. (eds.) Classic Works of the Dempster-Shafer Theory of Belief Functions, vol. 219, pp. 57–72. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Fernando, I., Henskens, F.A.: A web-based flatform for collaborative development of a knowledgebase for psychiatric case formulation and treatment decision support. In: IADIS e-Health 2012 International Conference, Lisban, Portugal (2012)Google Scholar
  8. 8.
    Fernando, I., Henskens, F.A., Cohen, M.: A domain specific conceptual model for a medical expert system in psychiatry, and a development framework. In: IADIS e-Health 2011 International Conference, Rome, Italy (2011)Google Scholar
  9. 9.
    Fernando, I., Henskens, F.A., Cohen, M.: A domain specific expert system model for diagnostic consultation in psychiatry. In: 12th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2011 (2011)Google Scholar
  10. 10.
    Godo, L., de Mántaras, R.L., Puyol-Gruart, J., Sierra, C.: Renoir, pneumon-ia and terap-ia: three medical applications based on fuzzy logic. Artificial Intelligence in Medicine 21, 153–162 (2001)CrossRefGoogle Scholar
  11. 11.
    Peirce, C.S.: Illustrations of the logic of science, sixth paper-deduction, induction, hypothesis. The Popular Science Monthly 1, 470–482 (1878)Google Scholar
  12. 12.
    Ramoni, M., Stefanelli, M., Magnani, L., Barosi, G.: An epistemological framework for medical knowledge-based systems. IEEE Transactions on Systems, Man and Cybernetics 22, 1361–1375 (1992)CrossRefGoogle Scholar
  13. 13.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press (1976)Google Scholar
  14. 14.
    Shortliffe, E.H., Buchanan, B.G.: A model of inexact reasoning in medicine. Mathematical Biosciences 23, 351–379 (1975)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Sugeno, M.: Industrial applications of fuzzy control. Elsevier Science (1985)Google Scholar
  16. 16.
    Todd, B.S., Stamper, R., Macpherson, P.: A probabilistic rule-based expert system. International Journal of Bio-Medical Computing 33, 129–148 (1993)CrossRefGoogle Scholar
  17. 17.
    Vetterlein, T., Ciabattoni, A.: On the (fuzzy) logical content of cadiag-2. Fuzzy Sets and Systems 161, 1941–1958 (2010)MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.School of Electrical Engineering & Computer ScienceUniversity of NewcastleCallaghanAustralia
  2. 2.The Mater Hospital, Hunter New England Area Health ServiceWaratahAustralia

Personalised recommendations