在联合学习环境中利用FHIR:为队列研究提供数据协调框架
Héctor Cadavid1, Bauke Arends2
1Netherlands eScience Center, The Netherlands.
Studies in health technology and informatics
|August 23, 2024
概括
这项研究引入了一个数据协调框架,用于早期发现心血管疾病. 它确保了像MyDigiTwin这样的联合学习平台中的数据一致性,安全地集成各种健康数据集.
科学领域:
- 数字健康数字健康
- 心血管疾病研究研究
- 联邦学习学习 (Federated Learning) 是一种学习方式.
背景情况:
- MyDigiTwin旨在通过荷兰个人健康环境 (PGOs) 托管的联合学习平台来预防心血管疾病.
- 确保PGOs和参考数据集之间的数据一致性是这种联合架构的一个关键挑战.
- 隐私保护是预测模型的联合学习方法的核心.
研究的目的:
- 引入一个新的数据协调框架,以有效生成基于FHIR的表示.
- 简化将多个队列研究数据集成到MyDigiTwin联合研究基础设施.
- 为应对心血管疾病研究的联合学习环境中数据一致性的挑战.
主要方法:
- 开发一个新的数据协调框架.
- 从队列研究中生成基于FHIR的数据表示.
- 应用框架,将生命线队列数据集成到MyDigiTwin.
主要成果:
- 拟议的框架使不同队列数据的有效协调成为可能.
- 基于FHIR的表示方便数据集成到联邦基础设施中.
- 在整合生命线数据方面证明了适用性,展示了更广泛使用的潜力.
结论:
- 数据协调框架有效地将异构的队列数据整合到联合研究中.
- 这种方法支持MyDigiTwin倡议,确保数据的一致性,并使隐私保护分析成为可能.
- 该框架有助于开发可靠的预测模型,用于早期发现心血管疾病.
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