FedEHR-Agents：面向自动化电子健康记录（EHR）建模的联邦智能体优化框架
FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modeling
- School of Computer Science, McGill University（麦吉尔大学计算机科学学院）
- Mila – Quebec AI Institute（米拉-魁北克人工智能研究所）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对医院EHR数据隐私与协作局限，提出以经验为中心的FedEHR-Agents框架，通过联邦智能体聚合建模经验，在多医院EHR基准上的临床预测任务中表现优于基线方法。
中文摘要 AI 辅助
大型语言模型的最新进展使自主临床智能体能够执行日益复杂的电子健康记录（EHR）建模工作流程。然而，部署在各医院的智能体受限于机构特定的数据和建模环境，而跨医院直接协作则受限于患者级EHR数据的敏感性。尽管联邦学习（FL）为隐私保护协作提供了自然基础，但现有方法仍以模型为中心，将联邦范围限制在预测模型或其更新上，却忽略了自主智能体积累的更丰富建模经验。为解决这一局限，我们提出FedEHR-Agents，一种以经验为中心的自动化EHR建模联邦智能体优化框架。每家医院部署一个自主临床EHR智能体，该智能体执行数据预处理和模型开发，同时通过历史记忆、特定任务评估和基于TextGrad的提示优化来完善本地临床建模经验。联邦服务器执行证据引导的经验聚合，以整合异构医院间可靠且互补的建模经验，并将聚合后的经验提炼为全局元提示，供后续本地优化使用。在真实多医院EHR基准上开展的大量实验表明，FedEHR-Agents在各类临床预测任务中始终优于本地和联邦基线方法，且在不同联邦规模和大语言模型（LLM）主干下均保持稳健。这些结果确立了临床建模经验作为超越传统以参数为中心的FL的有前景协作对象，并指向联邦自主临床智能的发展方向。
英文摘要
Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modeling workflows. However, agents deployed at individual hospitals remain constrained by institution-specific data and modeling environments, while direct cross-hospital collaboration is restricted by the sensitivity of patient-level EHR data. Although federated learning (FL) provides a natural foundation for privacy-preserving collaboration, existing approaches remain predominantly model-centric, limiting federation to prediction models or their updates while overlooking the richer modeling experience accumulated by autonomous agents. To address this limitation, we propose FedEHR-Agents, an experience-centric federated agentic optimization framework for automated EHR modeling. Each hospital deploys an autonomous clinical EHR agent that performs data preprocessing and model development while refining local clinical modeling experience through historical memory, task-specific evaluation, and TextGrad-based prompt refinement. The federated server performs evidence-guided experience aggregation to integrate reliable and complementary modeling experience across heterogeneous hospitals and distills the aggregated experience into global meta-prompts for subsequent local refinement. Extensive experiments on real-world multi-hospital EHR benchmarks demonstrate that FedEHR-Agents consistently outperforms local and federated baselines across diverse clinical prediction tasks and remains robust across different federation scales and LLM backbones. These results establish clinical modeling experience as a promising collaborative object beyond conventional parameter-centric FL and point toward federated autonomous clinical intelligence.
