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
Qi Huiying(), Guo Jianguang
School of Foundational Education, Peking University Health Science Center, Beijing 100191, China
Download: PDF (1157 KB)   HTML ( 8
Export: BibTeX | EndNote (RIS)      
Abstract  

[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
ZTFLH:  G350  

Cite this article:

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

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1321     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I5/88

标准名称 英文名称 简称 功能
研究数据表格模型 Study Data Tabulation Model SDTM 包括全部CDISC模型/标准所涉及的标准词汇和编码集
临床数据采集标准 Clinical Data Acquisition Standards
Harmonization
CDASH 用于病例报告表中基础数据收集字段的内容标准
操作数据模型 Operational Data Model ODM 基于XML, 用于获取、交换、报告或递交以及对基于病例报告表的临床研究数据归档的内容和格式标准
CDISC术语词典 用于解释与临床研究信息电子获取、交换、报告相关的术语及其定义
收集数据 CDASH变量名 SDTM变量 说明
不良事件编号 aespid AESPID 记录受试者每个不良事件的唯一标识符。
不良事件名称 aeterm AETERM 不良事件的名称。
不良事件开始日期 aestdat AESTDTC 不良事件开始时间。
不良事件结束日期 aeendat AEENDTC 不良事件被解决的时间。
不良事件的严重程度 aesev AESEV 描述不良事件的严重程度。
不良事件是否与先天性异常或
出生缺陷有关?
aescong AESCONG 记录“严重”不良事件是否与先天性异常或出生缺陷有关。
不良事件是否与研究治疗有关? aerel AEREL 临床医生/研究者判断研究治疗与不良事件之间是否存在因果
关系。
不良事件的结局? aeout AEOUT 描述与不良事件相关的受试者的状况。
字段名 字段类型 字段说明
ID 自动编号 主键字段
TableName Varchar(20) 电子病历数据库中表名
FormDef Varchar(20) 电子病历数据库中表对应的CRF
Name Varchar(20) 电子病历数据库表中字段名对应CRF一个条目
ItemDef Varchar(20) CRF中的一个条目的ID号
DataType Varchar(20) CRF中的一个条目的数据类型
Length Varchar(20) CRF中的一个条目的长度
[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.
doi: 10.1016/j.jbi.2015.06.023
[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.
doi: 10.4338/ACI-2012-03-RA-0011
[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.
doi: 10.1186/s12911-015-0163-5
[15] REDCap [EB/OL]. [2018-01-05] .
[1] Wang Hao, Lin Kerou, Meng Zhen, Li Xinlei. Identifying Multi-Type Entities in Legal Judgments with Text Representation and Feature Generation[J]. 数据分析与知识发现, 2021, 5(7): 10-25.
[2] Yu Xuehan, He Lin, Xu Jian. Extracting Events from Ancient Books Based on RoBERTa-CRF[J]. 数据分析与知识发现, 2021, 5(7): 26-35.
[3] Hu Haotian,Ji Jinfeng,Wang Dongbo,Deng Sanhong. An Integrated Platform for Food Safety Incident Entities Based on Deep Learning[J]. 数据分析与知识发现, 2021, 5(3): 12-24.
[4] Xue Fuliang,Liu Lifang. Fine-Grained Sentiment Analysis with CRF and ATAE-LSTM[J]. 数据分析与知识发现, 2020, 4(2/3): 207-213.
[5] Ma Jianxia,Yuan Hui,Jiang Xiang. Extracting Name Entities from Ecological Restoration Literature with Bi-LSTM+CRF[J]. 数据分析与知识发现, 2020, 4(2/3): 78-88.
[6] Na Ma,Zhixiong Zhang,Pengmin Wu. Automatic Identification of Term Citation Object with Feature Fusion[J]. 数据分析与知识发现, 2020, 4(1): 89-98.
[7] 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.
[8] 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.
[9] Feng Guoming,Zhang Xiaodong,Liu Suhui. DBLC Model for Word Segmentation Based on Autonomous Learning[J]. 数据分析与知识发现, 2018, 2(5): 40-47.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn