title: Medical Inpatient Journey Modeling and Clustering: A Bayesian Hidden Markov Model Based Approach.
abstract: Modeling and clustering medical inpatient journeys is useful to healthcare organizations for a number of reasons including inpatient journey reorganization in a more convenient way for understanding and browsing, etc. In this study, we present a probabilistic model-based approach to model and cluster medical inpatient journeys. Specifically, we exploit a Bayesian Hidden Markov Model based approach to transform medical inpatient journeys into a probabilistic space, which can be seen as a richer representation of inpatient journeys to be clustered. Then, using hierarchical clustering on the matrix of similarities, inpatient journeys can be clustered into different categories w.r.t their clinical and temporal characteristics. We evaluated the proposed approach on a real clinical data set pertaining to the unstable angina treatment process. The experimental results reveal that our method can identify and model latent treatment topics underlying in personalized inpatient journeys, and yield impressive clustering quality.
authors.affiliation: College of Biomedical Engineering and Instrument Science, Zhejiang University.
authors.forename: Zhengxing
authors.initials: Z
authors.lastname: Huang
authors.affiliation: Department of Cardiology, Chinese PLA General Hospital.
authors.forename: Wei
authors.initials: W
authors.lastname: Dong
authors.affiliation: Department of Computer Science and Engineering, University of Connecticut Abstract.
authors.forename: Fei
authors.initials: F
authors.lastname: Wang
authors.affiliation: College of Biomedical Engineering and Instrument Science, Zhejiang University.
authors.forename: Huilong
authors.initials: H
authors.lastname: Duan
mesh_terms: D000465:Algorithms; D000789:Angina, Unstable / Q000628:therapy; D001499:Bayes Theorem; D016000:Cluster Analysis; D057286:Electronic Health Records; D006801:Humans; D007297:Inpatients; D008390:Markov Chains; D015233:Models, Statistical; D008962:Models, Theoretical