Multidimensional temporal mining in clinical data

Authors:
Shusaku Tsumoto;Shoji Hirano
Affiliations:
Shimane University, Izumo, Japan;Shimane University, Izumo, Japan
Venue:
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Year:
2012

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Abstract

Since medical and healthcare data include temporal trends of clinical symptoms, temporal data mining is one of the most important elements to discover knowledge. In this paper, we propose a three-dimensional trajectories mining method to capture the similarities between temporal trajectories of three selected variables. The method was evaluated on two datasets: one is on chronic hepatitis, and the other is on temporal trends of #orders in our university hospital. The results showed that, compared with conventional studies, the method gave more detailed classification of temporal trends.