Cumulus:一个基于EHR的联合学习系统,由FHIR和AI提供动力
Andrew J McMurry1,2, Daniel I Gottlieb1,3, Timothy A Miller1,2
1Computational Health Informatics Program, Boston Children's Hospital, Boston, MA.
medRxiv : the preprint server for health sciences
|February 19, 2024
概括
库穆卢斯软件使用云技术和人工智能使电子健康记录 (EHR) 的可扩展,联合数据共享成为可能. 这种开源解决方案可以促进人口健康研究,同时通过汇总数据共享来保护患者的隐私.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 大规模的电子健康记录 (EHR) 数据交换面临重大挑战.
- 跨医疗保健系统的标准化数据访问对研究和公共卫生至关重要.
研究的目的:
- 开发和测试一个开源的,云托管的应用程序 ("收听器") 访问标准化EHR数据.
- 推进一个可扩展的,联合模型用于数据共享和学习使用AI.
主要方法:
- 使用容器化,云托管的软件 (Cumulus),安装在医疗机构的安全信封中.
- 通过SMART/HL7批量FHIR访问API访问EHR数据以进行自动处理和共享.
- 使用人工智能和自然语言处理工具进行非结构化文本分析.
主要成果:
- 在与公共卫生组织合作的情况下,Cumulus在五个医疗保健系统中部署.
- 根据人口健康研究感兴趣的变量分层生成的综合患者人数.
- 所有代码都作为开源提供,政策确保只剩下综合数据留给机构.
结论:
- 通过利用标准API,云计算和AI进步,Cumulus有效地解决了数据共享障碍.
- 该软件展示了可扩展性的潜力,以支持跨多种网络配置和用例的学习.
- 仅共享汇总数据的政策大大促进了数据共享协议和隐私保护.
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