针对医疗保健领域的人工智能的AIR·MS数据平台
Pablo Guerrero1,2, Morten Ernebjerg1, Thomas Holst1
1D4L Data4Life gGmbH, Potsdam, Brandenburg 14482, Germany.
JAMIA open
|November 21, 2025
概括
人工智能准备的西奈山 (AIR·MS) 平台统一了临床数据和计算资源,使大规模的人工智能研究成为可能. 它在各种医疗保健数据集中展示了队列构建和人工智能模型培训的高性能.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的人工智能
- 数据科学数据科学数据科学
背景情况:
- 医疗保健系统产生大量的不同临床数据.
- 整合不同的数据源和计算基础设施对于先进的AI研究至关重要.
- 现有的平台往往缺乏复杂的AI模型开发所需的规模和灵活性.
研究的目的:
- 为了引入人工智能准备的西奈山 (AIR·MS) 平台.
- 展示各种临床数据集和计算基础设施的整合.
- 通过三个人工智能研究项目来证明平台的实用性.
主要方法:
- AIR·MS集成结构化EHR数据和非结构化病理/放射学数据,使用OMOP共同数据模型.
- 数据存储在内存列式数据库中,元数据链接原始源数据.
- 符合HIPAA的云和本地HPC环境支持数据访问和分析.
主要成果:
- 该平台可访问超过1200万个患者记录,包括电子病历,临床笔记和成像元数据.
- AIR·MS支持具有高系统性能的交互式队列构建和AI模型培训.
- 三个用例展示了风险因素发现和联合心血管风险建模.
结论:
- AIR·MS成功地整合了临床数据和基础设施,用于大规模的人工智能研究.
- 该平台的可扩展性,安全性和协作设计可以作为未来举措的典范.
- AIR·MS使人工智能驱动的医疗保健研究能够在多式联络临床数据上进行.
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