开发联合分析的原型,以加强隐私,并使可信的访问COVID-19研究数据
Solmaz Eradat Oskoui1, Matthew Retford2, Eoghan Forde3
1Aridhia Informatics, UK; Usher Institute, University of Edinburgh, Edinburgh, UK.
International journal of medical informatics
|December 6, 2024
概括
联邦网络可以在没有数据传输的情况下进行安全的健康数据分析,试点为多国COVID-19研究提供新的基础设施. 这种方法增强了数据隐私,并促进了对关键公共卫生挑战的合作研究.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 流行病学 流行病学
背景情况:
- 联合网络通过消除物理数据传输来最大限度地降低敏感数据披露风险.
- 联邦分析是一种增强隐私的技术,可以在不移动源数据的情况下进行可靠的数据分析.
研究的目的:
- 概述国际COVID-19数据联盟 (ICODA) 在试点联邦网络基础设施中的方法.
- 测试使用国际围产期疫情结果研究 (iPOP) 数据的联合分析.
- 记录联邦网络的挑战和好处,用于分析跨国健康数据.
主要方法:
- 在安全匿名信息连接 (SAIL) 数据库和ICODA工作台之间建立了一个联合网络.
- 使用来自iPOP研究的总量级模型输出进行试点联合分析.
- 专注于技术,治理和用户体验的实施,用于未来的研究.
主要成果:
- 成功开发并试点一个联合网络和分析基础设施.
- 证明了使用个人级数据集的未来研究的基本步骤.
- 验证了对多个国家健康数据的联合分析的使用.
结论:
- 建立联合网络需要在治理,技术,培训,资源和资金方面进行大量投资.
- 将联合网络集成到长期计划中,为多个地点的研究合作提供了一个安全的平台.
- 联邦网络释放了健康数据的潜力,用于解决公共卫生问题的协作研究,同时确保隐私.
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