title: Similarity measure between patient traces for clinical pathway analysis: problem, method, and applications.
abstract: Clinical pathways leave traces, described as event 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 (CPA), which mainly focus on looking at aggregated data seen from an external perspective. Most existing methods measure similarities between patient traces via computing the relative distance between their event sequences. However, clinical pathways, as typical human-centered processes, always take place in an unstructured fashion, i.e., clinical events occur arbitrarily without a particular order. Bringing order in the chaos of clinical pathways may decline the accuracy of similarity measure between patient traces, and may distort the efficiency of further analysis tasks. In this paper, we present a behavioral topic analysis approach to measure similarities between patient traces. More specifically, a probabilistic graphical model, i.e., latent Dirichlet allocation (LDA), 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 provides a basis for further applications in CPA. In particular, three possible applications are introduced in this paper, i.e., patient trace retrieval, clustering, and anomaly detection. The proposed approach and the presented applications are evaluated via a real-world dataset of several specific clinical pathways collected from a Chinese hospital.
authors.forename: Zhengxing
authors.initials: Z
authors.lastname: Huang
authors.forename: Wei
authors.initials: W
authors.lastname: Dong
authors.forename: Huilong
authors.initials: H
authors.lastname: Duan
authors.forename: Haomin
authors.initials: H
authors.lastname: Li
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019091:Critical Pathways; D016208:Databases, Factual; D006801:Humans; D020300:Intracranial Hemorrhages; D008492:Medical Informatics Computing; D008962:Models, Theoretical