从用例到基础设施:数据驱动的生物医学研究优先事项的跨机构调查
Raja Mazumder1, Jonathon Keeney2, Luke Johnson1
1McCormick Genomics and Proteomics Center, Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, United States.
Journal of the American Medical Informatics Association : JAMIA
|January 20, 2026
概括
一项调查确定了对生物医学数据安全,联合分析的关键需求. 这些发现指导了联合生态系统分析和标准化技术 (FEAST) 协作研究平台的发展.
科学领域:
- 生物医学信息学 生物医学信息学
- 数据科学数据科学数据科学
- 计算生物学 计算生物学
背景情况:
- 分析和标准化技术联合生态系统 (FEAST) 是一个基于云的模块化平台,用于对现实世界生物医学数据进行安全的联合分析.
- 通过ARPA-H生物医学数据结构倡议开发的FEAST旨在解决访问和分析敏感健康信息的挑战.
研究的目的:
- 系统地识别和优先考虑各种生物医学领域的授权访问数据的研究需求.
- 通过跨机构调查指导和完善FEAST平台的模块化设计.
- 发现基础设施缺口,数据整合挑战和转化机会.
主要方法:
- 一项结构化调查分发给多个机构的研究人员.
- 受访者提供了有关数据类型,项目目标,分析方法和技术障碍的详细信息.
- 分析了收集的回复,以确定隐私,互操作性,可扩展性和可重现性的共同需求.
主要成果:
- 编制了61个用例,涵盖基因组学,成像学,临床表型学,EHR分析和精准医学.
- 关键需求包括多模式数据集成,HL7基于FHIR的安全访问,以及没有保留PII的联合模型培训.
- 观察到对符合FAIR基础设施的持续需求以及对现实世界数据分析的准备.
结论:
- 基于现实世界的数据需求,FEAST使用案例调查提供了生物医学信息学优先事项的观点.
- 这些发现为开发联合,保护隐私的基础设施提供了蓝图.
- 开发的基础设施将支持安全,协作和可扩展的生物医学研究.
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