@techreport{oai:ipsj.ixsq.nii.ac.jp:00050442,
 author = {TuBaoHo and NguyenTrongDung and Saori, Kawasaki and NguyenDucDung and Tu, BaoHo and Nguyen, TrongDung and Saori, Kawasaki and Nguyen, DucDung},
 issue = {30(2002-ICS-132)},
 month = {Mar},
 note = {The hepatitis temporal database collected at Chiba University gospital during 1982-2001 was recently given to challenge the KDD research. The database is large where each patient corresponds to 983 tests as sequences of values with different lengths and irregular time-stamp points. This paper presents a temporal abstraction approach mining knowledge from this hepatitis database. Exploiting hepatitis background knowledge and data analysis  we introduce method for charactering short-term changed and long-term changed tests. The transformed data allows us to apply different machine learning methods for finding knowledge of part of which is considered as new and interesting by medical doctors., The hepatitis temporal database collected at Chiba University gospital during 1982-2001 was recently given to challenge the KDD research. The database is large where each patient corresponds to 983 tests as sequences of values with different lengths and irregular time-stamp points. This paper presents a temporal abstraction approach mining knowledge from this hepatitis database. Exploiting hepatitis background knowledge and data analysis, we introduce method for charactering short-term changed and long-term changed tests. The transformed data allows us to apply different machine learning methods for finding knowledge of part of which is considered as new and interesting by medical doctors.},
 title = {Mining Hepatitis Data with Temporal Abstraction},
 year = {2003}
}