A Knowledge Scout for Discovering Medical Patterns: Methodology and System SCAMP

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dc.contributor.authorKaufman, Kenneth A.
dc.contributor.authorMichalski, Ryszard S.
dc.date.accessioned2006-11-03T18:17:08Z
dc.date.available2006-11-03T18:17:08Z
dc.date.issued2000-10
dc.description.abstractKnowledge 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.
dc.description.sponsorshipThis research was conducted in the Machine Learning and Inference Laboratory at George Mason University under partial support from the National Science Foundation under Grants No. IIS-0012121, IIS-9904078 and IRI-9510644.
dc.format.extent2113 bytes
dc.format.extent98083 bytes
dc.format.mimetypetext/xml
dc.format.mimetypeapplication/pdf
dc.identifier.citationKaufman, K. and Michalski, R. S., "A Knowledge Scout for Discovering Medical Patterns: Methodology and System SCAMP," Proceedings of the Fourth International Conference on Flexible Query Answering Systems, FQAS'2000, Warsaw, Poland, pp. 485-496, October 25-28, 2000.
dc.identifier.urihttps://hdl.handle.net/1920/1469
dc.language.isoen_US
dc.relation.ispartofseriesP 00-12
dc.titleA Knowledge Scout for Discovering Medical Patterns: Methodology and System SCAMP
dc.typePresentation

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