Reasoning and acquisition of knowledge in a system for hand wound diagnosis and prognosis

  • M. Michalewicz
  • M. A. Klopotek
  • S. T. Wierzchoń
Communications 4B Learning and Knowledge Discovery
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1609)


The paper describes a diagnostic system for hand wounds. The system shall not only propose diagnostic decisions (because this would not be sufficient for starting a complex therapeutic treatment), but shall also provide with a number of subdiagnoses describing results of subtests and makes available tools for knowledge acquisition from data. The system, implemented as a CGI service, possesses an interface based on an HTML-browser in an Internet-like network.

It consists of a bilingual database, a dictionary subsystem, a deterministic expert system, a Bayesian network base expert system, an automatic Bayesian network acquisition module and a visual Bayesian network editor.

Key words

Intelligent Information Systems Knowledge Representation and Integration Learning and Knowledge Discovery reasoning diagnostic systems Bayesian networks internet 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Acid S., de Campos L.M.: Approximations of Causal Networks by Polytrees: an Empirical Study. IMPU’94, Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 972–977, Paris, 1994Google Scholar
  2. 2.
    Bertele, U., and Brioschi, F. Nonserial Dynamic Programming, Academic Press, 1972.Google Scholar
  3. 3.
    Buchman B., Shortliffe E.: Rule-based Expert Systems, Addison-Wesley, 1984.Google Scholar
  4. 4.
    Chow C.K., Liu C.N.: Approximating discrete probability distributions with dependence trees, IEEE Transactions on Information Theory, Vol. IT-14, No. 3, (1968), 462–467MATHCrossRefGoogle Scholar
  5. 5.
    Jensen V.F. An introduction to Bayesian networks, University College Press, London, 1996.Google Scholar
  6. 6.
    Kłopotek M.A., Wierzchoń S.T., Michalewicz M.: Internet WWW as a new challange and chance for construction of friendly expert systems (in Polish). Proceedings of All-Polish Symposium “Artifical Intelligence and System Development” (CIR’96) Siedlce, 19–20 Sept. 1996, pp. 285–294.Google Scholar
  7. 7.
    Lauritzen S.L., Spiegelhalter D.J.: Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society B 50 (1988) 157–224.MATHMathSciNetGoogle Scholar
  8. 8.
    Pearl J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo CA, 1988Google Scholar
  9. 9.
    Pople, Jr., H. E.: Heuristic methods for imposing structure on ill-structured problems: The structuring of medical diagnostics. In Szolovits P., editor, Artificial Intelligence in Medicine, pp. 119–189. Westview Press, Boulder, Colorado, 1982.Google Scholar
  10. 10.
    Shafer G., Shenoy P.P., Mellouli K.: Propagating belief functions in quantitative Markov trees, International Journal of Approximate Reasoning. 1987:1, (4), 349–400.MATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    Shenoy, P.P.: Valuation-based systems: A framework for managing uncertainty in expert systems, in: L.A. Zadeh and J. Kacorzyk (eds.), Fuzzy Logic for the Management of Uncertainty, J. Wiley & Sons, New York, 1992, pp. 83–104.Google Scholar
  12. 12.
    Spirtes P., Glymour C., Scheines R.: Causation, Prediction and Search, Lecture Notes in Statistics 81, Springer-Verlag, 1993.Google Scholar
  13. 13.
    Valiveti R.S., Oommen B.J.: On using the chi-squared statistics for determining statistic dependence, Pattern Recognition Vol. 25 No. 11 (1992), 1389–1400.CrossRefGoogle Scholar
  14. 14.
    Wierzchoń S.T., Kłopotek M.A., Michalewicz M.Strużyna J.: Implementation of a bilingual educational expert system in a medical domain (in Polish), Proceedings of All-Polish AI Symposium (CIR’96) Siedlce, 19–20 Sept. 1996, pp. 235–244Google Scholar
  15. 15.
    Wierzchoń, S.T.: Constraint propagation over restricted space of configurations, in: R.R. Yager, J. Kacprzyk, and M. Fedrizzi (eds.) Advances in Dempster-Shafer Theory of Evidence, J. Wiley & Sons, New York 1994, pp. 375–394.Google Scholar
  16. 16.
    Wierzchoń S.T.: Methods for Representing and Processing Uncertain Information in the Dempster-Shafer Framework (in Polish), ICS PAS Press:, Warszawa, 1996.Google Scholar
  17. 17.
    Wierzchoń S.T., Kłopotek M.A., Michalewicz M.: A genetic-based algorithm for facts explanation in graphoidal expert systems, in: Proc. of 3-rd Biennial Joint Conference on Engineering Systems Design and Analysis, Montpelier, France 1996.Google Scholar
  18. 18.
    Wierzchoń S.T., Kłopotek M.A.: A Bilingual Educational Expert System For Hand Wounds Initial Diagnosis. Journal of Knowledge-based Intelligent Engineering Systems. Vol. 2 No. 4 (1998), pp. 211–222.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • M. Michalewicz
    • 1
  • M. A. Klopotek
    • 1
  • S. T. Wierzchoń
    • 1
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

Personalised recommendations