Disease Definition Based on Spiral Discovery of Exceptions(Medical Active Mining)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
スポンサーリンク
概要
- 論文の詳細を見る
In this paper, we show our endeavor for defining a disease by removing exceptional patients in a spiral manner. The removal is based on a likelihood-based criterion and can be sup-ported by our previously developed data mining methods and medical experts. Two series of experiments with the chronic hepatitis data showed that our proposed method is effective and promising from various viewpoints.
- 社団法人電子情報通信学会の論文
- 2004-11-27
著者
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YOKO Hideto
Chiba University Hospital
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TAKABAYASHI Katsuhiko
Chiba University Hospital
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YOKOI Hideto
Chiba University Hospital
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Zhong Ning
Maebashi Institute Of Technology
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JUMI MASATOSHI
Yokohama National University
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SUZUKI EINOSHIN
Yokohama National University
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OHSHIMA MUNEAKI
Maebashi Institute of Technology
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Suzuki Einoshin
Graduate School Of Engineering Yokohama National University
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- Integration of Learning Methods, Medical Literature and Expert Inspection in Medical Data Mining
- Temporal Logic in Mining Hepatitis Data(Medical Active Mining)
- Temporal Logic in Mining Hepatitis Data(Medical Active Mining)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
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- Development and Evaluation of an Integrated Time-Series KDD Environment : A Case Study of Medical KDD on Hepatitis(Medical Data Mining)
- Development and Evaluation of an Integrated Time-Series KDD Environment : A Case Study of Medical KDD on Hepatitis(Medical Data Mining)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
- Disease Definition Based on Spiral Discovery of Exceptions (Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ and IEICE-SIGAI on Active Mining) -- (Session 2: Medical Active Mining)
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