Discovery of Risky Cases in Chronic Diseases: An Approach Using Trajectory Grouping
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.
KeywordsDissimilarity Matrix Segment Pair Perform Cluster Analysis Chronic Hepatitis Patient Segment Parameter
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- 1.Ueda, N., Suzuki, S.: A Matching Algorithm of Deformed Planar Curves Using Multiscale Convex/Concave Structures. IEICE Transactions on Information and Systems J73-D-II(7), 992–1000 (1990)Google Scholar
- 2.Lindeberg, T.: Scale-Space for Discrete Signals. IEEE Trans. PAMI 12(3), 234–254 (1990)Google Scholar
- 3.Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis, 4th edn. Arnold Publishers (2001)Google Scholar
- 5.Tsumoto, S., Hirano, S., Takabayashi, K.: Development of the Active Mining System in Medicine Based on Rough Sets. Journal of Japan Society of Artificial Intelligence 20(2), 203–210 (2005)Google Scholar