加强医疗保健数据隐私和与联合学习的互操作性.
Adil Akhmetov1, Zohaib Latif1, Benjamin Tyler1
1Department of Computer Science, Nazarbayev University, Astana, Kazakhstan.
PeerJ. Computer science
|June 26, 2025
概括
联合学习 (FL) 与快速医疗互操作性资源 (FHIR) 结合,通过对可穿戴传感器数据的隐私保护分析来增强数字健康. 这种方法与预测建模的集中学习性能相匹配或超过.
科学领域:
- 数字健康数字健康
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 由于隐私和互操作性问题,大量的数字健康数据,特别是来自可穿戴传感器的数据,未得到充分利用.
- 现有的系统难以在异质平台上分析和交换数据,限制了电子病历和移动健康应用的利用.
研究的目的:
- 开发一个集成联合学习 (FL) 和快速医疗互操作性资源 (FHIR) 的新平台,以解决医疗数据未充分利用的问题.
- 为了使可穿戴传感器数据的协作,保护隐私的模型培训,同时确保数据标准化和互操作性.
主要方法:
- 开发了一个结合FL和FHIR的融合平台,利用本地模型学习来增强数据隐私.
- 为实践演示,创建了一个兼容FL和集中式学习 (CL) 的AutoML驱动的Web应用程序.
- 经验评估使用标准分类和回归指标比较FL和CL模型.
主要成果:
- 联合学习模型实现了与集中式学习模型相比或更好的分类准确性.
- 在回归任务中,FL模型表现与CL模型相同.
- 该平台成功地展示了对体力活动和能源消耗进行预测建模的可行性,并遵守FHIR标准.
结论:
- 与FHIR集成的联合学习平台为未来的数字健康生态系统提供了实际框架.
- 这种方法通过确保互操作性和保护用户隐私来优化连接健康数据的使用.
- 该研究强调了FL和FHIR在释放数字健康数据的全部价值方面的潜力.
关键词:
人工智能的人工智能是人工智能.数据互操作性数据的互操作性.数据挖掘和机器学习数据科学是数据科学.分布式和并行计算新兴技术 新兴技术联合学习是联合学习.物联网的物联网,就是物联网.科学计算和模拟技术可穿戴式传感器更多相关视频
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