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.
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References
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)
Eich, H.P., Ohmann, C.: Internet-Based Decision-Support Server for Acute Abdominal Pain. Artificial Intelligence in Medicine 20, 23–36 (2000)
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)
Puppe, F.: Knowledge Reuse Among Diagnostic Problem-Solving Methods in the Shell-Kit D3. Int. J. Human-Computer Studies 49, 627–649 (1998)
Puppe, F., Ziegler, S., Martin, U., Hupp, J.: Wissensbasierte Diagnosesysteme im Service- Support. Springer, Heidelberg (2001)
Pople, R.M., Myers, H.E.: Internist-1, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine. NEJM 307, 468–476 (1982)
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)
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)
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)
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)
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)
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Atzmueller, M., Baumeister, J., Puppe, F. (2003). Inductive Learning of Simple Diagnostic Scores. In: Perner, P., Brause, R., Holzhütter, HG. (eds) Medical Data Analysis. ISMDA 2003. Lecture Notes in Computer Science, vol 2868. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39619-2_4
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DOI: https://doi.org/10.1007/978-3-540-39619-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20282-0
Online ISBN: 978-3-540-39619-2
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