Discovery of Risky Cases in Chronic Diseases: An Approach Using Trajectory Grouping

  • Shoji Hirano
  • Shusaku Tsumoto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4914)


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.


Dissimilarity Matrix Segment Pair Perform Cluster Analysis Chronic Hepatitis Patient Segment Parameter 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shoji Hirano
    • 1
  • Shusaku Tsumoto
    • 1
  1. 1.Department of Medical InformaticsShimane University, School of MedicineIzumoJapan

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