You Seng Chan, Lee Seongwon, Cho Soo-Yeon, Park Hojun, Jung Sungjae, Cho Jaehyeong, Yoon Dukyong, Park Rae Woong
Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.
Center for Education Artificial Intelligence, Dankook University, Yongin, Korea.
Stud Health Technol Inform. 2017;245:467-470.
It is increasingly necessary to generate medical evidence applicable to Asian people compared to those in Western countries. Observational Health Data Sciences a Informatics (OHDSI) is an international collaborative which aims to facilitate generating high-quality evidence via creating and applying open-source data analytic solutions to a large network of health databases across countries. We aimed to incorporate Korean nationwide cohort data into the OHDSI network by converting the national sample cohort into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM). The data of 1.13 million subjects was converted to OMOP-CDM, resulting in average 99.1% conversion rate. The ACHILLES, open-source OMOP-CDM-based data profiling tool, was conducted on the converted database to visualize data-driven characterization and access the quality of data. The OMOP-CDM version of National Health Insurance Service-National Sample Cohort (NHIS-NSC) can be a valuable tool for multiple aspects of medical research by incorporation into the OHDSI research network.
与西方国家相比，生成适用于亚洲人群的医学证据变得越来越有必要。观察性健康数据科学与信息学（OHDSI）是一个国际合作项目，旨在通过创建和应用开源数据分析解决方案，为跨国家的大型健康数据库网络提供支持，从而促进高质量证据的生成。我们的目标是通过将韩国全国队列数据转换为观察性医疗结局合作组织通用数据模型（OMOP-CDM），将其纳入OHDSI网络。113万受试者的数据被转换为OMOP-CDM，平均转化率为99.1%。在转换后的数据库上使用基于开源OMOP-CDM的数据剖析工具ACHILLES，以可视化数据驱动的特征并评估数据质量。通过纳入OHDSI研究网络，国家健康保险服务-国家样本队列（NHIS-NSC）的OMOP-CDM版本可以成为医学研究多个方面的宝贵工具。
