Abstract
This paper presents a cluster analysis method for multidimensional time-series data of laboratory tests. Our method represents the time series of test results as trajectories in multidimensional space, and compares their structural similarity by using the multiscale comparison technique. It enables us to find the part-to-part correspondences between two trajectories, while taking into account the relationships between different tests. The resultant dissimilarity can be further used with clustering algorithms, for finding the groups of similar cases. The method was applied to the cluster analysis of Albumin-Platelet data in chronic hepatitis patients, and could form interesting groups of cases that have high correspondence to their fibrotic stages.
