Spiral discovery of a separate prediction model from chronic hepatitis data

Authors:
Masatoshi Jumi;Einoshin Suzuki;Muneaki Ohshima;Ning Zhong;Hideto Yokoi;Katsuhiko Takabayashi
Affiliations:
Electrical and Computer Engineering, Yokohama National University, Japan;Electrical and Computer Engineering, Yokohama National University, Japan;Faculty of Engineering, Maebashi Institute of Technology, Japan;Faculty of Engineering, Maebashi Institute of Technology, Japan;Division of Medical Informatics, Chiba University Hospital, Japan;Division of Medical Informatics, Chiba University Hospital, Japan
Venue:
JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
Year:
2003

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Abstract

In this paper, we summarize our endeavor for spiral discovery of a separate prediction model from chronic hepatitis data. We have initially proposed various learning/discovery methods including time-series decision tree, PrototypeLines, and peculiarity-oriented mining method for mining the data. This experience has motivated us to model physicians as considering typical cases with the specific disease and ruling out clearly exceptional cases. We have developed a spiral discovery system which learns a prediction model for each type of cases, and obtained promising results from experiments.