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6 April 2000 Discovery of approximate concepts in clinical databases based on a rough set model
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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.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shusaku Tsumoto "Discovery of approximate concepts in clinical databases based on a rough set model", Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000);


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