Research Papers
Possibility of Integrated Data Mining of Clinical Data
Authors:
Akinori Abe ,
International Research and Educational Institute for Integrated Medical Science (IREIIMS), Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162-8666 JAPAN
ATR Knowledge Science Laboratories, 2-2-2, Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288 JAPAN
Norihiro Hagita,
International Research and Educational Institute for Integrated Medical Science (IREIIMS), Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162-8666 JAPAN
ATR Intelligent Robotics and Communication Laboratories, 2-2-2, Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288 JAPAN
Michiko Furutani,
International Research and Educational Institute for Integrated Medical Science (IREIIMS), Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162-8666 JAPAN
Yoshiyuki Furutani,
International Research and Educational Institute for Integrated Medical Science (IREIIMS), Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162-8666 JAPAN
Rumiko Matsuoka
International Research and Educational Institute for Integrated Medical Science (IREIIMS), Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162-8666 JAPAN
Abstract
In this paper, we introduce integrated data mining. Because of recent rapid progress in medical science as well as clinical diagnosis and treatment, integrated and cooperative research among medical researchers, biology, engineering, cultural science, and sociology is required. Therefore, we propose a framework called Cyber Integrated Medical Infrastructure (CIMI). Within this framework, we can deal with various types of data and consequently need to integrate those data prior to analysis. In this study, for medical science, we analyze the features and relationships among various types of data and show the possibility of integrated data mining.
How to Cite:
Abe, A., Hagita, N., Furutani, M., Furutani, Y. and Matsuoka, R., 2007. Possibility of Integrated Data Mining of Clinical Data. Data Science Journal, 6, pp.S104–S115. DOI: http://doi.org/10.2481/dsj.6.S104
Published on
28 Mar 2007.
Peer Reviewed
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