Unsupervised Grouping of Trajectory Data on Laboratory Examinations for Finding Exacerbating Cases in Chronic Diseases

In this paper we present a method for finding exacerbating cases in chronic diseases based on the cluster analysis technique. Cluster analysis of time series hospital examination data is still a challenging task as it requires comparison of data involving temporal irregulariry and multidimensionalty. Our method first maps a set of time series containing different types of laboratory tests into a directed trajectory representing the time course of patient status. Then the trajectories for individual patients are compared in multiscale and grouped into similar cases. Experimental results on synthetic digit-stroke data showed that our method could yield low error rates (0.016±0.014 for classification and 0.118±0.057 for cluster rebuild). Results on the chronic hepatitis dataset demonstrated that the method could discover the groups of excacerbating cases based on the similarity of ALB-PLT trajectories.