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Proceedings Paper

Discovery of approximate concepts in clinical databases based on a rough set model
Author(s): Shusaku Tsumoto
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Paper Abstract

Rule discovery methods have been introduced to find useful and unexpected patterns from databases. However, one of the most important problems on these methods is that extracted rules have only positive knowledge, which do not include negative information that medical experts need to confirm whether a patient will suffer from symptoms caused by drug side-effect. This paper first discusses the characteristics of medical reasoning and defines positive and negative rules based on rough set model. Then, algorithms for induction of positive and negative rules are introduced. Then, the proposed method was evaluated on clinical databases, the experimental results of which shows several interesting patterns were discovered, such as a rule describing a relation between urticaria caused by antibiotics and food.

Paper Details

Date Published: 6 April 2000
PDF: 8 pages
Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000); doi: 10.1117/12.381765
Show Author Affiliations
Shusaku Tsumoto, Shimane Medical Univ. (Japan)

Published in SPIE Proceedings Vol. 4057:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology II
Belur V. Dasarathy, Editor(s)

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