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Methods Inf Med 2017; 56(S 01): e49-e66
DOI: 10.3414/ME16-01-0047
Original Articles
Schattauer GmbH

Utilizing Electronic Medical Records to Discover Changing Trends of Medical Behaviors Over Time

Liangying Yin
1   College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
,
Zhengxing Huang
1   College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
3   College of Medical Engineering Technology, Xinjiang Medical University, Urumqi, China
,
Wei Dong
2   Department of Cardiology, Chinese PLA General Hospital, Beijing, China
,
Chunhua He
3   College of Medical Engineering Technology, Xinjiang Medical University, Urumqi, China
,
Huilong Duan
1   College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
3   College of Medical Engineering Technology, Xinjiang Medical University, Urumqi, China
› Author Affiliations Funding: This work was supported by the National Nature Science Foundation of China under Grant No 61672450 and 61562088.
Further Information

Publication History

received: 19 April 2016

accepted: 12 May 2016

Publication Date:
31 January 2018 (online)

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Summary

Objectives: Medical behaviors are playing significant roles in the delivery of high quality and cost-effective health services. Timely discovery of changing frequencies of medical behaviors is beneficial for the improvement of health services. The main objective of this work is to discover the changing trends of medical behaviors over time.

Methods: This study proposes a two-steps approach to detect essential changing patterns of medical behaviors from Electronic Medical Records (EMRs). In detail, a probabilistic topic model, i.e., Latent Dirichlet allocation (LDA), is firstly applied to disclose yearly treatment patterns in regard to the risk stratification of patients from a large volume of EMRs. After that, the changing trends by comparing essential/critical medical behaviors in a specific time period are detected and analyzed, including changes of significant patient features with their values, and changes of critical treatment interventions with their occurring time stamps.

Results: We verify the effectiveness of the proposed approach on a clinical dataset containing 12,152 patient cases with a time range of 10 years. Totally, 135 patients features and 234 treatment interventions in three treatment patterns were selected to detect their changing trends. In particular, evolving trends of yearly occurring probabilities of the selected medical behaviors were categorized into six content changing patterns (i.e, 112 growing, 123 declining, 43 up-down, 16 down-up, 35 steady, and 40 jumping), using the proposed approach. Besides, changing trends of execution time of treatment interventions were classified into three occurring time changing patterns (i.e., 175 early-implemented, 50 steady-implemented and 9 delay-implemented).

Conclusions: Experimental results show that our approach has an ability to utilize EMRs to discover essential evolving trends of medical behaviors, and thus provide significant potential to be further explored for health services redesign and improvement.

Keywords

Changing pattern detection - Electronic Medical Records - Latent Dirichlet allocation - medical behavior

 

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