title: Incorporating comorbidities into latent treatment pattern mining for clinical pathways.
abstract: In healthcare organizational settings, the design of a clinical pathway (CP) is challenging since patients following a particular pathway may have not only one single first-diagnosis but also several typical comorbidities, and thus it requires different disciplines involved to put together their partial knowledge about the overall pathway. Although many data mining techniques have been proposed to discover latent treatment information for CP analysis and reconstruction from a large volume of clinical data, they are specific to extract nontrivial information about the therapy and treatment of the first-diagnosis. The influence of comorbidities on adopting essential treatments is crucial for a pathway but has seldom been explored. This study proposes to extract latent treatment patterns that characterize essential treatments for both first-diagnosis and typical comorbidities from the execution data of a pathway. In particular, we propose a generative statistical model to extract underlying treatment patterns, unveil the latent associations between diagnosis labels (including both first-diagnosis and comorbidities) and treatments, and compute the contribution of comorbidities in these patterns. The proposed model extends latent Dirichlet allocation with an additional layer for diagnosis modeling. It first generates a set of latent treatment patterns from diagnosis labels, followed by sampling treatments from each pattern. We verify the effectiveness of the proposed model on a real clinical dataset containing 12,120 patient traces, which pertain to the unstable angina CP. Three treatment patterns are discovered from data, indicating latent correlations between comorbidities and treatments in the pathway. In addition, a possible medical application in terms of treatment recommendation is provided to illustrate the potential of the proposed model. Experimental results indicate that our approach can discover not only meaningful latent treatment patterns exhibiting comorbidity focus, but also implicit changes of treatments of first-diagnosis due to the incorporation of typical comorbidities potentially.
authors.affiliation: College of Biomedical Engineering and Instrument Science, Zhejiang University, China; College of Medical Engineering Technology, Xinjiang Medical University, China. Electronic address: zhengxinghuang@zju.edu.cn.
authors.forename: Zhengxing
authors.initials: Z
authors.lastname: Huang
authors.affiliation: Department of Cardiology, Chinese PLA General Hospital, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Dong
authors.affiliation: IT Department, Chinese PLA General Hospital, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Ji
authors.affiliation: College of Medical Engineering Technology, Xinjiang Medical University, China.
authors.forename: Chunhua
authors.initials: C
authors.lastname: He
authors.affiliation: College of Biomedical Engineering and Instrument Science, Zhejiang University, China.
authors.forename: Huilong
authors.initials: H
authors.lastname: Duan
mesh_terms: D015897:Comorbidity; D019091:Critical Pathways / Q000706:statistics & numerical data*; D057225:Data Mining / Q000379:methods*; D006801:Humans; D008490:Medical Informatics; D015233:Models, Statistical
keywords: Clinical pathway; Comorbidity; Latent Dirichlet allocation; Treatment pattern mining