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

  • Kenneth A. Kaufman
  • Ryszard S. Michalski
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 7)

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.

Keywords

Association Rule Query Language Conceptual Cluster Database Operator Asthma Condition 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Kenneth A. Kaufman
    • 1
  • Ryszard S. Michalski
    • 1
    • 2
  1. 1.Machine Learning and Inference LaboratoryGeorge Mason UniversityFairfaxUSA
  2. 2.Polish Academy of SciencesInstitute of Computer ScienceWarsawPoland

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