Please wait a minute...
Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (5): 88-93    DOI: 10.11925/infotech.2096-3467.2017.1321
Orginal Article Current Issue | Archive | Adv Search |
Integrating Multi-Source Clinical Research Data Based on CDISC Standard
Huiying Qi(),Jianguang Guo
School of Foundational Education, Peking University Health Science Center, Beijing 100191, China
Download: PDF(1157 KB)   HTML ( 2
Export: BibTeX | EndNote (RIS)      

[Objective] This study explores new ways to integrate multi-source clinical research data based on CDISC standard. [Context] The proposed method simplifies the procedures of submitting research data to the drug regulatory department and speeds up the listing of new drugs. It also promotes the sharing of data from different studies. [Methods] First, we designed a CRF based on the CDISC CDASH standard. Then, we mapped the electronic medical records to the CRF in accordance with the ODM standard. Third, we integrated the medical records with the clinical experimental data in the EDC system. Finally, all data were stored in the standard SDTM format database. [Results] We successfully integrated data from different systems into a CDISC database. [Conclusions] The proposed method effectively integrates electronic medical records and clinical experimental data. It helps us avoid entering duplicated data and improves the efficiency of clinical research.

Key wordsCDISC      Data Integration      EDC      CRF      CDASH     
Received: 26 December 2017      Published: 20 June 2018

Cite this article:

Huiying Qi,Jianguang Guo. Integrating Multi-Source Clinical Research Data Based on CDISC Standard. Data Analysis and Knowledge Discovery, 2018, 2(5): 88-93.

URL:     OR

[1] Carlson D, Farkash A, Timm J T.A Model-driven Approach for Biomedical Data Integration[J]. Studies in Health Technology and Informatics, 2010, 160(2): 1164-1168.
[2] Health Level Seven International. Introduction to HL7 Standards [EB/OL]. [2018-01-05]. .
[3] Jc C R, Staubert S, Lobe M, et al.Automated Import of Clinical Data from HL7 Messages into OpenClinica and tranSMART Using Mirth Connect[J]. Studies in Health Technology and Informatics , 2016, 228: 317-321.
[4] The World Wide Web Consortium. OWL [EB/OL].[2018-01-05]. .
[5] Kunz I, Lin M, Frey L J, et al.Metadata Mapping and Reuse in caBIG[J]. BMC Bioinformatics, 2009, 10(2): 1-11.
[6] Stausberg J, Lobe M, Verplancke P, et al.Foundations of a Metadata Repository for Databases of Registers and Trials[J]. Studies in Health Technology and Informatics, 2009, 150: 409-413.
[7] Jiang G, Solbrig H R, Iberson-Hurst D, et al.A Collaborative Framework for Representation and Harmonization of Clinical Study Data Elements Using Semantic Media Wiki[C]// Proceedings of AMIA Joint Summits on Translational Science. 2010: 11-15.
[8] Clinical Data Interchange Standards Consortium. CDISC. Mission & Principles [EB/OL]. [2018-01-05]. .
[9] Huser V, Sastry C, Breymaier M, et al.Standardizing Data Exchange for Clinical Research Protocols and Case Report Forms: An Assessment of the Suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM)[J]. Journal of Biomedical Informatics, 2015, 57: 88-99.
[10] Breil B, Kenneweg J, Fritz F, et al.Multilingual Medical Data Models in ODM Format: A Novel Form-based Approach to Semantic Interoperability Between Routine Healthcare and Clinical Research[J]. Applied Clinical Informatics, 2012, 3(3): 276-289.
[11] Doods J, Neuhaus P, Dugas M, et al.Converting ODM Metadata to FHIR Questionnaire Resources[J]. Studies in Health Technology and Informatics, 2016, 228: 456-460.
[12] Clinical Data Interchange Standards Consortium. CDASH v1.1 [EB/OL]. [2018-01-05]. .
[13] Clinical Data Interchange Standards Consortium. Controlled Terminology [EB/OL].[2018-01-05]. .
[14] Dugas M.ODM2CDA and CDA2ODM: Tools to Convert Documentation Forms Between EDC and EHR Systems[J]. BMC Medical Informatics and Decision Making, 2015, 15(1): 40.
[15] REDCap [EB/OL]. [2018-01-05] .
[1] Xiaoxiao Zhu,Zunqi Yang,Jing Liu. Construction of an Adverse Drug Reaction Extraction Model Based on Bi-LSTM and CRF[J]. 数据分析与知识发现, 2019, 3(2): 90-97.
[2] Li Yu,Li Qian,Changlei Fu,Huaming Zhao. Extracting Fine-grained Knowledge Units from Texts with Deep Learning[J]. 数据分析与知识发现, 2019, 3(1): 38-45.
[3] Guoming Feng,Xiaodong Zhang,Suhui Liu. DBLC Model for Word Segmentation Based on Autonomous Learning[J]. 数据分析与知识发现, 2018, 2(5): 40-47.
[4] Wang Miping,Wang Hao,Deng Sanhong,Wu Zhixiang. Extracting Chinese Metallurgy Patent Terms with Conditional Random Fields[J]. 现代图书情报技术, 2016, 32(6): 28-36.
[5] Sui Mingshuang,Cui Lei. Extracting Chemical and Disease Named Entities with Multiple-Feature CRF Model[J]. 现代图书情报技术, 2016, 32(10): 91-97.
[6] Duan Yufeng, Zhu Wenjing, Chen Qiao, Liu Wei, Liu Fenghong. The Study on Out-of-Vocabulary Identification on a Model Based on the Combination of CRFs and Domain Ontology Elements Set[J]. 现代图书情报技术, 2015, 31(4): 41-49.
[7] Shi Cui, Wang Yang, Yang Bin, Yao Ye. Identification of Non-nest Coordination for Chinese Patent Literature[J]. 现代图书情报技术, 2014, 30(10): 76-83.
[8] Wang Run,He Lin,Wang Dongbo,Huang Shuiqing,Fan Yuanbiao. Research on Plant Growth and Development Stage Named Entity Recognition for Text Mining[J]. 现代图书情报技术, 2014, 30(1): 24-27.
[9] Guan Xiaoda,Lv Xueqiang,Li Zhuo,Zheng Luexing,. Chinese Organization Name Recognition in User Query Log[J]. 现代图书情报技术, 2014, 30(1): 72-78.
[10] Meng Meiren, Ding Shengchun. Research on the Credibility of Online Chinese Product Reviews[J]. 现代图书情报技术, 2013, 29(9): 60-66.
[11] Gu Jun, Xu Xin. Study on Ontology Relation Extraction in Chinese Patent Documents[J]. 现代图书情报技术, 2013, 29(10): 73-78.
[12] Lu Wanhui, Ma Jianxia. Research on Complex Time Information Extraction Based on CRF Model[J]. 现代图书情报技术, 2011, 27(10): 29-33.
[13] Zheng Rongting Li Nan Ji Jiuming Teng Qingqing. Research on Recognition of Chinese Chemical Substance Names[J]. 现代图书情报技术, 2010, 26(6): 48-52.
[14] Wang Mingsen,Wang Qiang. Research on the Construction of Mashup System[J]. 现代图书情报技术, 2009, 25(5): 34-38.
[15] Liu Kun,Lv Xueqiang,Wang Tao,Shi Shuicai. Binarization for Document Image Based on Multi-scale Conditional Random Fields[J]. 现代图书情报技术, 2009, 25(4): 79-81.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938