在生命科学中共享敏感数据:集中和联合方法的概述
Maria A Rujano1, Jan-Willem Boiten2, Christian Ohmann1
1European Clinical Research Infrastructure Network (ECRIN), Boulevard Saint Jacques 30, 75014, Paris, France.
Briefings in bioinformatics
|June 5, 2024
概括
对敏感的生物医学数据进行集中和联合数据共享模型的比较显示出明显的优势. 集中式模型在数据链接和协调方面表现出色,而联合式模型则为研究提供了可扩展性和法律合规性.
科学领域:
- 生物医学信息学 生物医学信息学
- 健康 数据科学 数据科学
- 计算生物学 计算生物学
背景情况:
- 来自各种来源 (成像,实验室,基因组学) 的生物医学数据具有巨大的研究潜力.
- 隐私问题和基础设施需求造成了敏感数据共享的障碍.
- 有效的数据共享对于科学突破和公共卫生至关重要.
研究的目的:
- 为了比较敏感的生物医学数据的集中和联合数据共享模型.
- 为选择合适的数据共享策略提供指导.
- 在欧洲健康数据共享倡议中为决策提供信息.
主要方法:
- 分析了五个大规模的,现实世界的欧洲数据共享用例.
- 对集中式和联合式数据共享模型进行比较检查.
- 基于数据链接,协调,互操作性,可扩展性和法律合规性的评估.
主要成果:
- 集中式模型有助于数据链接,协调和互操作性.
- 联合模型通过保持数据去中心化来提高可扩展性和法律合规性.
- 使用案例包括法国健康数据中心,BBMRI-ERIC,EGA,OHDSI和EBRAINS.
结论:
- 集中式和联合式模型在敏感的生物医学数据共享方面都有明显的优势.
- 集中式方法对数据集成是有效的,而联合式模型支持更广泛的采用和控制.
- 该研究提供了基于特定需求和约束的优化数据共享策略的见解.
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