Discovery of risky cases in chronic diseases: an approach using trajectory grouping

Authors:
Shoji Hirano;Shusaku Tsumoto
Affiliations:
Department of Medical Informatics, Shimane University, School of Medicine, Izumo, Shimane, Japan;Department of Medical Informatics, Shimane University, School of Medicine, Izumo, Shimane, Japan
Venue:
JSAI'07 Proceedings of the 2007 conference on New frontiers in artificial intelligence
Year:
2007

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Abstract

This paper presents an approach to finding risky cases in chronic diseases using a trajectory grouping technique. Grouping of trajectories on hospital laboratory examinations is still a challenging task as it requires comparison of data with mutidimensionalty and temporal irregulariry. Our method first maps a set of time series containing different types of laboratory tests into directed trajectories representing the time course of patient states. Then the trajectories for individual patients are compared in multiscale and grouped into similar cases. Experimental results on the chronic hepatitis data demonstrated that the method could find the groups of discending trajectories that well corresponded to the cases of higher fibrotic stages.