Abstract
Knowledge scouts are software agents that autonomously synthesize knowledge of interest to a given user (target knowledge) by applying inductive database operators to a local or distributed dataset. This paper describes briefly a method and a scripting language for developing knowledge scouts, and then reports on experiments with a knowledge scout, SCAMP, for discovering patterns characterizing relationships among lifestyles, symptoms and diseases in a large medical database. Discovered patterns are presented in two forms: (1) attributional rules, which are expressions in attributional calculus, and (2) association graphs, which graphically and abstractly represent relations expressed by the rules. Preliminary results indicate a high potential utility of the presented methodology for deriving useful and understandable knowledge.
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Kaufman, K.A., Michalski, R.S. (2001). A Knowledge Scout for Discovering Medical Patterns: Methodology and System SCAMP. In: Larsen, H.L., Andreasen, T., Christiansen, H., Kacprzyk, J., Zadrożny, S. (eds) Flexible Query Answering Systems. Advances in Soft Computing, vol 7. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1834-5_45
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DOI: https://doi.org/10.1007/978-3-7908-1834-5_45
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