用于多中心临床预测的层次重量化个性化联合学习
Xuebing Yang1, Duanchang Wan2, Gang Han3
1Guangzhou University, Guangzhou, 510006, China; University of Chinese Academy of Sciences, Beijing, 100049, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Computer methods and programs in biomedicine
|August 26, 2025
概括
通过解决数据异质性,FedRew增强了电子健康记录 (EHR) 的个性化联合学习. 这种个性化的联合学习方法改善了多个医疗中心的临床预测.
科学领域:
- 人工智能
- 机器学习
- 卫生信息学
背景情况:
- 电子健康记录 (EHR) 对于临床预测至关重要,但通常分布在多个医疗中心.
- 联合学习可以在没有数据共享的情况下进行协作模型培训,但多中心电子健康记录数据的异质性带来了挑战.
- 由于患者数据在各机构之间存在差异,现有的方法难以达到令人满意的预测性能.
研究的目的:
- 开发个性化联合学习 (PFL) 方法,
- 为了改善临床预测,应对多中心电子健康记录数据异质性的挑战.
- 训练每个客户的个别模型,在他们的特定数据上表现良好.
主要方法:
- 提出了FedRew,一种基于模型无关的元学习的个性化联合学习 (PFL) 方法.
- 在联合培训过程中实现了对本地适应和全球聚合的等级重量.
- 采用替代最小化方案进行样本重量和不断更新的聚合重量机制.
主要成果:
- 与基线和最先进的PFL方法相比,FedRew在eICU-CRD数据集上的平均性能和平均排名都更高.
- 在医院预测死亡率的平均AUROC为0.894.
- 预测剩余时间的平均RMSE为1.464.
结论:
- 在多中心的电子健康记录中,FedRew有效处理数据异质性.
- 该方法显示了重症监护室 (ICU) 临床预测任务的竞争性性能.
- 在分布式医疗环境中,FedRew具有显著的潜力来改进个性化临床预测.
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