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Discovery of Trends and States in Irregular Medical Temporal Data

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Discovery Science (DS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2843))

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Abstract

Temporal abstraction has been known as a powerful approach of data abstraction by converting temporal data into interval with abstracted values including trends and states. Most temporal abstraction methods, however, has been developed for regular temporal data, and they cannot be used when temporal data are collected irregularly. In this paper we introduced a temporal abstraction approach to irregular temporal data inspired from a real-life application of a large database in hepatitis domain.

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Author information

Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa, 923-1292, Japan

    Trong Dung Nguyen, Saori Kawasaki & Tu Bao Ho

Authors
  1. Trong Dung Nguyen
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  2. Saori Kawasaki
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  3. Tu Bao Ho
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Editor information

Editors and Affiliations

  1. FG Knowledge Engineering, FB Informatik, Technical University Darmstadt, Hochschulstr. 10, 64289, Darmstadt

    Gunter Grieser

  2. Meme Media Laboratory, Hokkaido University, N13 W8, 0608628, Sapporo, Japan

    Yuzuru Tanaka

  3. Graduate School of Informatics, Kyoto University Yoshida Honmachi, Sakyo-ku, 606-850, Kyoto, Japan

    Akihiro Yamamoto

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© 2003 Springer-Verlag Berlin Heidelberg

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Nguyen, T.D., Kawasaki, S., Ho, T.B. (2003). Discovery of Trends and States in Irregular Medical Temporal Data. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds) Discovery Science. DS 2003. Lecture Notes in Computer Science(), vol 2843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39644-4_40

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  • DOI: https://doi.org/10.1007/978-3-540-39644-4_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20293-6

  • Online ISBN: 978-3-540-39644-4

  • eBook Packages: Springer Book Archive

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