Inductive Learning of Simple Diagnostic Scores

  • Martin Atzmueller
  • Joachim Baumeister
  • Frank Puppe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2868)

Abstract

Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semi-automatic learning methods can be used to support the expert.

We propose diagnostic scores as a promising approach and present a method for inductive learning of diagnostic scores. It can be be refined incrementally by applying different types of background knowledge. We give an evaluation of the presented approach with a real-world case base.

References

  1. 1.
    Ohmann, C., et al.: Clinical Benefit of a Diagnostic Score for Appendicitis: Results of a Prospective Interventional Study. Archives of Surgery 134, 993–996 (1999)CrossRefGoogle Scholar
  2. 2.
    Eich, H.P., Ohmann, C.: Internet-Based Decision-Support Server for Acute Abdominal Pain. Artificial Intelligence in Medicine 20, 23–36 (2000)CrossRefGoogle Scholar
  3. 3.
    Buscher, H.P., Engler, C., Fuhrer, A., Kirschke, S., Puppe, F.: HepatoConsult: A Knowledge-Based Second Opinion and Documentation System. Artificial Intelligence in Medicine 24, 205–216 (2002)CrossRefGoogle Scholar
  4. 4.
    Puppe, F.: Knowledge Reuse Among Diagnostic Problem-Solving Methods in the Shell-Kit D3. Int. J. Human-Computer Studies 49, 627–649 (1998)CrossRefGoogle Scholar
  5. 5.
    Puppe, F., Ziegler, S., Martin, U., Hupp, J.: Wissensbasierte Diagnosesysteme im Service- Support. Springer, Heidelberg (2001)Google Scholar
  6. 6.
    Pople, R.M., Myers, H.E.: Internist-1, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine. NEJM 307, 468–476 (1982)CrossRefGoogle Scholar
  7. 7.
    Pople, H.E.: Heuristic Methods for Imposing Structure on Ill-Structured Problems: The Structuring of Medical Diagnostics. In: Szolovits, P. (ed.) Artificial Intelligence in Medicine, AAAS/Westview Press (1982)Google Scholar
  8. 8.
    Neumann, M., Baumeister, J., Liess, M., Schulz, R.: An Expert System to Estimate the Pesticide Contamination of Small Streams using Benthic Macroinvertebrates as Bioindicators, Part 2. Ecological Indicators 2, 391–401 (2003)CrossRefGoogle Scholar
  9. 9.
    Fronhöfer, B., Schramm, M.: A Probability Theoretic Analysis of Score Systems. In: Kern-Isberner, G., Lukasiewicz, T., Weydert, E. (eds.) KI-2001 Workshop: Uncertainty in Artificial Intelligence, pp. 95–108 (2001)Google Scholar
  10. 10.
    Schramm, M., Ertel, W.: Reasoning with Probabilities andMaximum Entropy: The System PIT and its Application in LEXMED. In: Inderfuth, K., et al. (eds.) Operations Research Proceeedings, pp. 274–280. Springer, Heidelberg (1999)Google Scholar
  11. 11.
    Baumeister, J., Atzmueller, M., Puppe, F.: Inductive Learning for Case-Based Diagnosis with Multiple Faults. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 28–42. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Martin Atzmueller
    • 1
  • Joachim Baumeister
    • 1
  • Frank Puppe
    • 1
  1. 1.Department of Computer ScienceUniversity of WürzburgWürzburgGermany

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