NDLI: Identifying Exacerbating Cases in Chronic Diseases Based on the Cluster Analysis of Trajectory Data on Laboratory Examinations

Content Provider	IEEE Xplore Digital Library
Author	Hirano, Shoji Tsumoto, Shusaku
Copyright Year	2007
Abstract	In this paper we present a cluster analysis method for multidimensional time-series medical data and its appli- cation to finding groups of exacerbating cases in chronic hepatitis. Our method represents time series laboratory ex- amination data of a patient as a trajectory. Compaison of trajectories is done using a two-stage approach. Firstly, it compares trajectories based on their structural similar- ity and determines the best correspondence of partial seg- ments. After that, it calculates the sum of value-based dis- similarities for all pairs of the matched segments as the final dissimilarity of the two trajectories, which can be used for clustering. Experimental results on a synthetic digit-stroke data provided low error ratio of 0.016 ±0.014 for classi- fication and 0.118 ±0.057 for cluster rebuild. Results on the chronic hepatitis dataset demonstrated that the method could discover the groups of exacerbating cases based on the similarity of ALB-PLT trajectories.
Starting Page	151
Ending Page	156
File Size	748957
Page Count	6
File Format	PDF
ISBN	9780769530192
DOI	10.1109/ICDMW.2007.105
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data analysis Multidimensional systems Liver diseases Shape Laboratories Time series analysis Medical tests Data mining Medical diagnostic imaging Testing
Content Type	Text
Resource Type	Article

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