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Automated revision of expert rules for treating acute abdominal pain in children

  • Sašo Džeroski
  • Giorgos Potamias
  • Vassilis Moustakis
  • Giorgos Charissis
Knowledge Acquisition and Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1211)

Abstract

Decision making knowledge acquired directly from a medical expert is often incorrect and incomplete. Another source of knowledge about a decision making problem are examples of expert decisions in situations that have occurred in practice, stored in patient records of clinical information systems. Such examples can be used to revise the expert-provided knowledge, i.e., to discover and repair its deficiences. The revised knowledge performs better than the original one and often better than rules learned from examples alone. In addition, it inherits parts of the original expert knowledge and is thus easier to understand and accept for the expert. We present an application of the machine learning approach of theory revision to the problem of revising an expert-provided theory for treating children with acute abdominal pain.

Keywords

Original Theory Acute Abdominal Pain Domain Theory Clinical Information System Revision Process 
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.

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References

  1. 1.
    Baffes, P.T., and Mooney, R.J. (1993). Symbolic revision of theories with M-of-N rules. In Proc. Thirteenth International Joint Conference on Artificial Intelligence, pp. 1135–1140. Chambery, France, 1993.Google Scholar
  2. 2.
    de Dombal, F.T. (1991). Diagnosis of abdominal pain. Churchil Livingstone, Singapore.Google Scholar
  3. 3.
    De Jong, J.F., and Mooney, R.J. (1986). Explanation based learning: an alternative view. Machine Learning, 1(2): 145–176.Google Scholar
  4. 4.
    Morik, K., Wrobel, S., Kietz, J.-U., and Emde, W. (1993). Knowledge Acquisition and Machine Learning — Theory, Methods, and Applications. Academic Press, London.Google Scholar
  5. 5.
    Potamias, G., Moustakis, V., and Charissis, G. (1996). Iterative knowledge construction and maintenance. In Proc. ICML'96 Workshop Machine Learning Meets Human Computer Interaction. Bari, Italy, 1996.Google Scholar
  6. 6.
    Richards, B.L., and Mooney, R.J. (1991). Refinement of first-order Horn-clause domain theories. Machine Learning, 19(2): 95–131.Google Scholar
  7. 7.
    Ourston, D., and Mooney, R.J. (1994). Theory refinement combining analytical and empirical methods. Artificial Intelligence, 66: 273–309.Google Scholar
  8. 8.
    Waldschmit, J., and Charissis, G. (1990). Das akute Abdomen im Kindesalter. Diagnose und Differentialdiagnose. Edition Medezin, New York.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Sašo Džeroski
    • 1
    • 2
  • Giorgos Potamias
    • 1
  • Vassilis Moustakis
    • 1
    • 3
  • Giorgos Charissis
    • 4
  1. 1.FORTH-ICSHeraklionGreece
  2. 2.Department of Intelligent SystemsJožef Stefan InstituteLjubljanaSlovenia
  3. 3.Department of Production and Management EngineeringTechnical University of CreteChaniaGreece
  4. 4.Pediatric Clinic, University Hospital, Medical SchoolUniversity of CreteHeraklionGreece

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