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FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modeling — arXiv:2608.27856

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.27856
License (as archived): CC0-1.0
Archived: 2026-10-17T10:43:12.982Z
SHA-256 of the archived text: 21f6598d37da76ac7f5f8fa162b9f68c704284dad56fbb223dffe738cc733301
Stable URL of this page: https://metraipolis.com/documento/dc_17dfc679e508

How to cite

FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modeling — arXiv:2608.27856 — arXiv (en), https://arxiv.org/abs/2608.27856 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_17dfc679e508 (Metraipolis archive dc_17dfc679e508, sha256 21f6598d37da76ac7f5f8fa162b9f68c704284dad56fbb223dffe738cc733301)

BibTeX

@misc{dc17dfc679e508,
  title = {FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modeling — arXiv:2608.27856},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.27856},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_17dfc679e508; sha256 21f6598d37da76ac7f5f8fa162b9f68c704284dad56fbb223dffe738cc733301},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.27856. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-31 in cs.MA).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2608.27856 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T10:33:20.606Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).