为心血管疾病预测进行联合学习:对基于生物信号和电子心血管系统的方法进行比较审查
Hagyeong Ryu1, Myungeun Lee1,2, Soo-Hyung Kim1
1Department of Artificial Intelligence Convergence, Chonnam National University, Buk-gu, Gwangju 61186, Republic of Korea.
Healthcare (Basel, Switzerland)
|November 13, 2025
概括
联合学习 (FL) 通过比较生物信号和电子健康记录 (EHR) 数据来推进心血管AI. 本综述为心脏预测中可靠,可解释和可扩展的FL提供了设计指南.
科学领域:
- 人工智能在医学中的应用
- 协作式机器学习 协作式机器学习
- 心血管疾病预测预测
背景情况:
- 联合学习 (FL) 能够实现多机构医疗AI,同时保护数据隐私.
- 现有的审查缺乏对心血管疾病的FL数据模式的系统比较.
- 这项研究通过比较基于生物信号和EHR的FL应用来弥补这一差距.
研究的目的:
- 综合审查和比较FL应用程序使用生物信号与EHR数据进行心血管疾病预测.
- 确定不同的FL方法的方法论策略,性能权衡和临床可行性.
- 提出以数据类型为中心的设计准则,以改善心血管AI中的FL.
主要方法:
- 在过去五年中对28项FL研究 (17项基于生物信号,11项基于EHR) 进行了系统审查.
- 分析包括客户端集群,异步学习,贝叶斯推理,自适应优化和安全聚合在内的方法.
- 在数据模式中对性能,隐私和可扩展性的比较评估.
主要成果:
- 基于生物信号的FL在使用物联网的个性化,低延迟的心脏监测 (例如,心电图分类) 中表现出色.
- 基于EHR的FL在大规模,维护隐私的医院合作和决策支持方面表现出强项.
- 关键技术包括生物信号的非IID数据缓解和EHR的分布式框架.
结论:
- 生物信号和EHR数据模式为心血管应用提供了FL的补充优势.
- FL框架需要定制的设计准则,以提高可靠性,可解释性和临床可扩展性.
- 这一审查为开发在心血管诊断和预测中更有效的FL解决方案提供了基础.
相关概念视频
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