Mining hepatitis data with temporal abstraction

Authors:
Tu Bao Ho;Trong Dung Nguyen;Saori Kawasaki;Si Quang Le;Dung Duc Nguyen;Hideto Yokoi;Katsuhiko Takabayashi
Affiliations:
Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa, Japan;Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa, Japan;Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa, Japan;Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa, Japan;Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa, Japan;Chiba University Hospital, Inohana, Chuo-ku, Chiba, Japan;Chiba University Hospital, Inohana, Chuo-ku, Chiba, Japan
Venue:
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2003

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Abstract

The hepatitis temporal database collected at Chiba university hospital between 1982--2001 was recently given to challenge the KDD research. The database is large where each patient corresponds to 983 tests represented as sequences of irregular timestamp points with different lengths. This paper presents a temporal abstraction approach to mining knowledge from this hepatitis database. Exploiting hepatitis background knowledge and data analysis, we introduce new notions and methods for abstracting short-term changed and long-term changed tests. The abstracted data allow us to apply different machine learning methods for finding knowledge part of which is considered as new and interesting by medical doctors.