Similarity Measuring between Patient Traces for Clinical Pathway Analysis

Abstract

Clinical pathways leave traces, described as activity sequences with regard to a mixture of various latent treatment behaviors. Measuring similarities between patient traces can profitably be exploited further as a basis for providing insights into the pathways, and complementing existing techniques of clinical pathway analysis, which mainly focus on looking at aggregated data seen from an external perspective. In this paper, a probabilistic graphical model, i.e., Latent Dirichlet Allocation, is employed to discover latent treatment behaviors of patient traces for clinical pathways such that similarities of pairwise patient traces can be measured based on their underlying behavioral topical features. The presented method, as a basis for further tasks in clinical pathway analysis, are evaluated via a real-world data-set collected from a Chinese hospital.