Discovery of clinical pathway patterns from event logs using probabilistic topic models.
Discovery of clinical pathway (CP) patterns has experienced increased attention over the years due to its importance for revealing the structure, semantics and dynamics of CPs, and to its usefulness for providing clinicians with explicit knowledge which can be directly used to guide treatment activities of individual patients. Generally, discovery of CP patterns is a challenging task as treatment behaviors in CPs often have a large variability depending on factors such as time, location and patient individual. Based on the assumption that CP patterns can be derived from clinical event logs which usually record various treatment activities in CP executions, this study proposes a novel approach to CP pattern discovery by modeling CPs using mixtures of an extension to the Latent Dirichlet Allocation family that jointly models various treatment activities and their occurring time stamps in CPs. Clinical case studies are performed to evaluate the proposed approach via real-world data sets recording typical treatment behaviors in patient careflow. The obtained results demonstrate the suitability of the proposed approach for CP pattern discovery, and indicate the promise in research efforts related to CP analysis and optimization.
College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqing Building 510, Zheda Road 38#, Hangzhou, Zhejiang 310008, China.
Zhengxing
Z
Huang
Department of Cardiology, Chinese PLA General Hospital, China.
Wei
W
Dong
IT Department, Chinese PLA General Hospital, China.
Lei
L
Ji
College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqing Building 510, Zheda Road 38#, Hangzhou, Zhejiang 310008, China.
Chenxi
C
Gan
College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqing Building 510, Zheda Road 38#, Hangzhou, Zhejiang 310008, China.
Xudong
X
Lu
College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqing Building 510, Zheda Road 38#, Hangzhou, Zhejiang 310008, China. Electronic address: duanhl@zju.edu.cn.
Huilong
H
Duan
D000465:Algorithms; D000789:Angina, Unstable / Q000175:diagnosis / Q000628:therapy*; D002681:China; D019091:Critical Pathways; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D015233:Models, Statistical; D009369:Neoplasms / Q000175:diagnosis / Q000628:therapy*; D057188:Workflow
Clinical event log; Clinical pathway analysis; Latent Dirichlet Allocation; Pattern discovery; Topic models