一个基于模型的开放信息存储库,用于对异质流行病的可持续再利用研究数据数据
Kristina Scheuermann1, Philip Hufeland2, Birger Haarbrandt1
1Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Germany.
Studies in health technology and informatics
|August 23, 2024
概括
下萨克森州的COVID-19研究网络 (COFONI) 开发了一个数据存储库,使用开放标准为伪名化数据. 该平台通过允许可访问的,跨地点的研究数据检索来增强流行病的准备.
科学领域:
- 公共卫生 公共卫生
- 传染病研究 传染病研究
- 医疗信息学 医疗信息学
背景情况:
- 随着COVID-19的爆发,人们越来越需要强大的研究基础设施.
- 有效的流行病准备需要快速获取多样化,跨地点的数据.
- 现有的数据库往往缺乏标准化和互操作性.
研究的目的:
- 为流行病研究和战略开发建立一个技术平台.
- 创建一个研究数据库,允许提供和检索假名化数据.
- 为 FAIR 数据原则利用开放标准和开源组件.
主要方法:
- 开发一个利用openEHR标准和EHRbase开源组件的研究数据库.
- 对患者数据实施伪名化,包括临床和社会人口统计信息.
- 集成AQL查询构建器和数据访问的研究请求流程.
主要成果:
- 建立了一个功能性研究数据库 (forschungsdb.cofoni.de),遵守 FAIR 标准.
- 该平台为异质数据集提供了跨位置数据检索的便利.
- 研究人员可以进行新的研究,组装队列,并出口定制的数据.
结论:
- COFONI平台提供了一个灵活,可扩展的流行病准备蓝图.
- 标准化,开放的数据表示增强了可访问性和研究能力.
- 该存储库支持快速响应和为未来的流行病制定战略.
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