Constructing Large Scale Cohort for Clinical Study on Heart Failure with Electronic Health Record in Regional Healthcare Platform: Challenges and Strategies in Data Reuse

Liu Daowen; Lei Liqi; Ruan Tong; He Ping

doi:10.24920/003579

Your Location：

Home >

Browse articles >

Constructing Large Scale Cohort for Clinical Study on Heart Failure with Electronic Health Record in Regional Healthcare Platform: Challenges and Strategies in Data Reuse

Chinese Medical Sciences Journal;Chinese Medical Sciences Journal Vol. 34, Issue 2, Pages: 90-102(2019)

Special Report | Updated：2024-04-10

- Constructing Large Scale Cohort for Clinical Study on Heart Failure with Electronic Health Record in Regional Healthcare Platform: Challenges and Strategies in Data Reuse
- Affiliations：
  
  1. School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
  2. Shanghai Hospital Development Center, Shanghai 200041, China
- Author bio：
  
  *，E-mail: ruantong@ecust.edu.cn.
- Funds：
- DOI：10.24920/003579
  CLC：
- Received：05 March 2019，
  
  Accepted：2019-4-23，
  
  Published Online：24 May 2019，
  
  Published：24 May 2019
- Accepted：
Scan QR Code
Liu Daowen, Lei Liqi, Ruan Tong, et al. Constructing Large Scale Cohort for Clinical Study on Heart Failure with Electronic Health Record in Regional Healthcare Platform: Challenges and Strategies in Data Reuse[J]. Chinese medical sciences journal;chinese medical sciences journal, 2019, 34(2): 90-102.
DOI：

Liu Daowen, Lei Liqi, Ruan Tong, et al. Constructing Large Scale Cohort for Clinical Study on Heart Failure with Electronic Health Record in Regional Healthcare Platform: Challenges and Strategies in Data Reuse[J]. Chinese medical sciences journal;chinese medical sciences journal, 2019, 34(2): 90-102. DOI： 10.24920/003579.

摘要

区域性卫生平台汇集了多家医院的电子健康档案数据，已被用于医疗卫生管理领域。在临床研究中进一步复用这些数据是目前临床科研的公共需求，但是需要面对电子健康档案中医疗术语的不一致性以及区域卫生平台中数据质量和数据格式多样化等方面的挑战。我们提出了基于区域卫生平台电子健康档案半自动构建大规模队列的流程与方法，作为临床流行病学疗效研究的基础。我们首先构建了一个中文医疗术语图谱，解决了区域医疗健康平台术语多样化的问题。其次，我们建立了利用中文术语知识图谱中的同义词关系和上下位关系归一化医疗术语来构建专病病例库的方法，并描述了构建了一个心力衰竭病例库的方法和步骤。根据一项观察他汀类药物对心力衰竭患者疗效的临床研究需求，我们基于此心力衰竭病例库，利用信息技术自动构建了一个由29647例心力衰竭患者数据构成的大型回顾性队列样本，并通过propensity score匹配获得了临床特征对等的病例组(n=6346)和对照组(n=6346)。以180天内再入院为结局指标，采用logistic回归分析发现，心力衰竭患者服用他汀类药物与180天内再入院有显著相关性（P<0.05）。本文为电子健康档案的大数据挖掘提供了工作流程和应用的范例。

Abstract

Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management. It is a common requirement to reuse the data for clinical research. However

we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform. In this paper

we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology. We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform. Secondly

we built special disease case repositories (i.e.

heart failure repository) that utilize the graph to search the related patients and to normalize the data. Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure

we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository. After the propensity score matching

the study group (

=6346) and the control group (

=6346) with parallel clinical characteristics were acquired. Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients. This paper presents the workflow and application example of big data mining based on regional EHR data.

关键词

Keywords

references

Shah AD , Langenberg C , Rapsomaniki E , et al . Type 2 diabetes and incidence of cardiovascular diseases: a cohort study in 1.9 million people . Lancet Diabetes Endocrinol 2015 ; 3 ( 2 ): 105 - 13 . doi: DOI: 10.1016/S2213-8587(14)70219-0 http://doi.org/10.1016/S2213-8587(14)70219-0 https://linkinghub.elsevier.com/retrieve/pii/S2213858714702190 https://linkinghub.elsevier.com/retrieve/pii/S2213858714702190

Denaxas SC , George J , Herrett E , et al . Data resource profile: cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER) . Int J Epidemiol 2012 ; 41 ( 6 ): 1625 - 38 . doi: DOI: 10.1093/ije/dys188 http://doi.org/10.1093/ije/dys188 https://academic.oup.com/ije/article-lookup/doi/10.1093/ije/dys188 https://academic.oup.com/ije/article-lookup/doi/10.1093/ije/dys188

Abrahão MTF , Nobre MRC , Gutierrez MA . A method for cohort selection of cardiovascular disease records from an electronic health record system . Int J Med Inform 2017 ; 102 : 138 - 49 . doi: DOI: 10.1016/j.ijmedinf.2017.03.015 http://doi.org/10.1016/j.ijmedinf.2017.03.015 https://linkinghub.elsevier.com/retrieve/pii/S138650561730076X https://linkinghub.elsevier.com/retrieve/pii/S138650561730076X

Jin B , Che C , Liu Z , et al . Predicting the risk of heart failure with EHR sequential data modeling . IEEE Access 2018 ; 6 : 9256 - 61 . doi: DOI: 10.1109/ACCESS.2017.2789324 http://doi.org/10.1109/ACCESS.2017.2789324 http://ieeexplore.ieee.org/document/8245772/ http://ieeexplore.ieee.org/document/8245772/

Lei L , Zhou Y , Zhai J , et al . An effective patient representation learning for time-series prediction tasks based on EHRs. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2018 Dec 3-6; Madric, Spain . IEEE ; 2018 . p 885 - 92 . doi: DOI: 10.1109/bibm.2018.8621542 http://doi.org/10.1109/bibm.2018.8621542 .

Rajkomar A , Oren E , Chen K , et al . Scalable and accurate deep learning with electronic health records . Digital Med 2018 ; 1 ( 1 ): 18 . doi: DOI: 10.1038/s41746-018-0029-1 http://doi.org/10.1038/s41746-018-0029-1

Donnelly K . SNOMED-CT: the advanced terminology and coding system for eHealth . Stud Health Technol Inform 2006 ; 121 : 279 - 90 .

Mcdonald CJ , Huff SM , Suico JG , et al . LOINC, a universal standard for identifying laboratory observations: a 5-year update . Clin Chem 2003 ; 49 ( 4 ): 624 - 33 . doi: DOI: 10.1373/49.4.624 http://doi.org/10.1373/49.4.624 http://www.clinchem.org/cgi/doi/10.1373/49.4.624 http://www.clinchem.org/cgi/doi/10.1373/49.4.624

De Franco E , Flanagan SE , Houghton JA , et al . The effect of early, comprehensive genomic testing on clinical care in neonatal diabetes: an international cohort study . Lancet 2015 ; 386 ( 9997 ): 957 - 63 . doi: DOI: 10.1016/S0140-6736(15)60098-8 http://doi.org/10.1016/S0140-6736(15)60098-8 https://linkinghub.elsevier.com/retrieve/pii/S0140673615600988 https://linkinghub.elsevier.com/retrieve/pii/S0140673615600988

Bashi N , Karunanithi M , Fatehi F , et al . Remote monitoring of patients with heart failure: an overview of systematic reviews . J Med Internet Res 2017 ; 19 ( 1 ): e18 . doi: DOI: 10.2196/jmir.6571 http://doi.org/10.2196/jmir.6571 http://www.jmir.org/2017/1/e18/ http://www.jmir.org/2017/1/e18/

Kudyba SP . Healthcare informatics : improving efficiency through technology, analytics, and management. Boca Raton, FL, USA: CRC Press ; 2016 . doi: DOI: 10.1201/b21424-6 http://doi.org/10.1201/b21424-6 .

Nakamura M , Wakabayashi G , Miyasaka Y , et al . Multicenter comparative study of laparoscopic and open distal pancreatectomy using propensity score‐matching . J Hepatobiliary Pancreat Sci 2015 ; 22 ( 10 ): 731 - 6 . doi: DOI: 10.1002/jhbp.268 http://doi.org/10.1002/jhbp.268 http://doi.wiley.com/10.1002/jhbp.268 http://doi.wiley.com/10.1002/jhbp.268

Ruan T , Wang M , Sun J , et al . An automatic approach for constructing a knowledge base of symptoms in Chinese . J Biomed Semantics 2017 ; 8 ( 1 ): 33 . doi: DOI: 10.1186/s13326-017-0145-x http://doi.org/10.1186/s13326-017-0145-x http://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-017-0145-x http://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-017-0145-x

Qiu J , Wang Q , Zhou Y , et al . Fast and accurate recognition of Chinese clinical named entities with residual dilated convolutions. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2018 Dec 3-6; Madrid, Spain . IEEE ; 2018 . p 935 - 42 . doi: DOI: 10.1109/bibm.2018.8621360 http://doi.org/10.1109/bibm.2018.8621360 .

Xu J , Gan L , Cheng M , et al . Unsupervised medical entity recognition and linking in Chinese online medical text . J Healthc Eng 2018 ; 2548537 . doi: DOI: 10.1155/2018/2548537 http://doi.org/10.1155/2018/2548537 .

Li Z , Yang Z , Shen C , et al . Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text . BMC Med Inform Decis Mak 2019 ; 19 ( Suppl 1 ): 22 . doi: DOI: 10.1186/s12911-019-0736-9 http://doi.org/10.1186/s12911-019-0736-9

Bodenreider O . The unified medical language system (UMLS): integrating biomedical terminology . Nucleic Acids Res 2004 ; 32 ( suppl 1 ): D267 - 70 . doi: DOI: 10.1093/nar/gkh061 http://doi.org/10.1093/nar/gkh061 https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkh061 https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkh061

Lowe HJ , Barnett GO . Understanding and using the medical subject headings (MeSH) vocabulary to perform literature searches . JAMA 1994 ; 271 ( 14 ): 1103 - 8 . DOI: 10.1001/jama.1994.03510380059038 http://doi.org/10.1001/jama.1994.03510380059038 http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.1994.03510380059038 http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.1994.03510380059038

Sherman RE , Anderson SA , Dal Pan GJ , et al . Real-world evidence—what is it and what can it tell us . N Engl J Med 2016 ; 375 ( 23 ): 2293 - 7 . doi: DOI: 10.1056/NEJMsb1609216 http://doi.org/10.1056/NEJMsb1609216 http://www.nejm.org/doi/10.1056/NEJMsb1609216 http://www.nejm.org/doi/10.1056/NEJMsb1609216

Samwald M , Jentzsch A , Bouton C , et al . Linked open drug data for pharmaceutical research and development . J Cheminform 2011 ; 3 ( 1 ): 19 . doi: DOI: 10.1186/1758-2946-3-19 http://doi.org/10.1186/1758-2946-3-19

Hearst MA . Automatic acquisition of hyponyms from large text corpora. Proceedings of the 14th conference on Computational linguistics. 1992 Aug. 23-28; Nantes, France. Stroudsburg, PA, USA: Association for computational linguistics ; 1992 . 2 :p 539 - 45 . doi: DOI: 10.3115/992133.992154 http://doi.org/10.3115/992133.992154 .

Belleau F , Nolin MA , Tourigny N , et al . Bio2RDF: towards a mashup to build bioinformatics knowledge systems . J Biomed Inform 2008 ; 41 ( 5 ): 706 - 16 . doi: DOI: 10.1016/j.jbi.2008.03.004 http://doi.org/10.1016/j.jbi.2008.03.004 https://linkinghub.elsevier.com/retrieve/pii/S1532046408000415 https://linkinghub.elsevier.com/retrieve/pii/S1532046408000415

Zhang J , Wang Q , Zhang Z , et al . An effective standardization method for the lab indicators in regional medical health platform using N-grams and stacking. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2018 Dec 3-6; Madrid, Spain . IEEE , 2018 . p. 1602 - 9 . doi: DOI: 10.1109/bibm.2018.8621274 http://doi.org/10.1109/bibm.2018.8621274 .

Wang Q , Wang T , Xu C . Using a knowledge graph for hypernymy detection between Chinese symptoms. 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI). 2018 Mar 29-31; Xiamen China . IEEE , 2018 . p. 601 - 6 . doi: DOI: 10.1109/icaci.2018.8377528 http://doi.org/10.1109/icaci.2018.8377528 .

Wang Q , Xu C , Zhou Y , et al . An attention-based Bi-GRU-CapsNet model for hypernymy detection between compound entities. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2018 Dec 3-6; Madrid, Spain . IEEE , 2018 . p. 1031 - 5 . doi: DOI: 10.1109/bibm.2018.8621408 http://doi.org/10.1109/bibm.2018.8621408 .

Richesson RL , Andrews JE , Krischer JP . Use of SNOMED CT to represent clinical research data: a semantic characterization of data items on case report forms in vasculitis research . J Am Med Inform Assoc 2006 ; 13 ( 5 ): 536 - 46 . doi: DOI: 10.1197/jamia.M2093 http://doi.org/10.1197/jamia.M2093 https://academic.oup.com/jamia/article-lookup/doi/10.1197/jamia.M2093 https://academic.oup.com/jamia/article-lookup/doi/10.1197/jamia.M2093

Weiskopf NG , Weng C . Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research . J Am Med Inform Assoc 2013 ; 20 ( 1 ): 144 - 51 . doi: DOI: 10.1136/amiajnl-2011-000681 http://doi.org/10.1136/amiajnl-2011-000681 https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2011-000681 https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2011-000681

Ye Q , Zhao L , Ruan T , et al . Usability research of regional health data for clinical efficacy analysis . Big Data Res 2018 ; 4 ( 3 ):2018026. Chinese. doi: DOI: 10.11959/j.issn.2096-0271.2018026 http://doi.org/10.11959/j.issn.2096-0271.2018026 .

Elwyn G , O’connor A , Stacey D , et al . Developing a quality criteria framework for patient decision aids: online international Delphi consensus process . BMJ 2006 ; 333 ( 7565 ): 417 . doi: 10.1136/bmj.38926.629329.ae 1001-9294-34-2-90/bmj.38926.629329.ae . DOI: 10.1136/bmj.38926.629329.AE http://doi.org/10.1136/bmj.38926.629329.AE http://www.bmj.com/cgi/doi/10.1136/bmj.38926.629329.AE http://www.bmj.com/cgi/doi/10.1136/bmj.38926.629329.AE

Brown BB . Delphi process: a methodology used for the elicitation of opinions of experts . Santa Monica, CA, USA: RAND Corporation ; 1968 . https://www.rand.org/pubs/papers/P3925.html . Accessed May 16 , 2019 . https://www.rand.org/pubs/papers/P3925.html https://www.rand.org/pubs/papers/P3925.html

Bauersachs J , Galuppo P , Fraccarollo D , et al . Improvement of left ventricular remodeling and function by hydroxymethylglutaryl coenzyme a reductase inhibition with cerivastatin in rats with heart failure after myocardial infarction . Circulation 2001 ; 104 ( 9 ): 982 - 5 . DOI: 10.1161/hc3401.095946 http://doi.org/10.1161/hc3401.095946 https://www.ahajournals.org/doi/10.1161/hc3401.095946 https://www.ahajournals.org/doi/10.1161/hc3401.095946

Caliendo M , Kopeinig S . Some practical guidance for the implementation of propensity score matching . J Eco Survey 2008 ; 22 ( 1 ): 31 - 72 . doi: DOI: 10.1007/3-540-28708-6_4 http://doi.org/10.1007/3-540-28708-6_4 http://www.blackwell-synergy.com/toc/joes/22/1 http://www.blackwell-synergy.com/toc/joes/22/1

Lunt M . Selecting an appropriate caliper can be essential for achieving good balance with propensity score matching . Am J Epidemiol 2013 ; 179 ( 2 ): 226 - 35 . doi: DOI: 10.1093/aje/kwt212 http://doi.org/10.1093/aje/kwt212 .

Views

1178

Downloads

CSCD

Alert me when the article has been cited

Submit

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰