发表机构
University of Florida; University of Arizona; University of Houston; University of North Carolina at Chapel Hill; University of Texas MD Anderson Cancer Center（佛罗里达大学; 亚利桑那大学; 休斯顿大学; 北卡罗来纳大学教堂山分校; 德克萨斯大学MD安德森癌症中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出知识引导型智能体NAS框架ATHENA，通过跨医院复用架构知识，在6项临床预测任务的12项评估中9项优于或匹配基线方法，可减少Transformer型EHR建模的手动架构调整。
AI 中文摘要
基于Transformer的模型被广泛用于从电子健康记录（EHR）中进行临床预测，但其架构仍需要大量手动调整，且最优配置可能因任务和医院而异。神经架构搜索（NAS）可自动完成架构设计，但传统方法对基于Transformer的EHR模型而言计算成本高昂。近期大语言模型（LLM）引导的NAS方法减少了手动搜索设计，但通常每次搜索独立进行，无法跨医院复用架构知识。本研究提出ATHENA（面向EHR神经架构搜索的跨医院智能体迁移），一种用于基于Transformer的EHR建模的知识引导型智能体NAS框架。ATHENA采用权重共享超网络，每所医院仅预训练一次，可将候选架构实例化为继承子网并通过微调评估，而非独立预训练。它还融入两层跨医院架构先验：第一层基于任务描述符从源站点检索高性能架构示例；第二层使用基于SHapley加性解释（SHAP）的元回归估计架构组件的效果。这些先验与目标医院的验证反馈共同指导多智能体LLM搜索。在6项临床预测任务和2个独立卫生系统中，当搜索预算为30时，ATHENA在12项医院-任务评估的9项中与4个NAS基线方法相当或更优，且在重复搜索中展现出更一致的架构选择。ATHENA为减少基于Transformer的EHR建模中的手动架构调整提供了实用方法，代码公开于指定URL。
英文摘要
Transformer-based models are widely used for clinical prediction from electronic health records (EHRs), yet their architectures require manual tuning, and the optimal configuration may vary across tasks and hospitals. Neural architecture search (NAS) automates architecture design, but conventional methods are computationally costly for Transformer-based EHR models. Recent large language model (LLM)-guided NAS methods reduce manual search design but conduct each search independently, without reusing architecture knowledge across hospitals. In this study, we propose ATHENA (Agentic Transfer across Hospitals for EHR Neural Architecture Search), a knowledge-guided agentic NAS framework for Transformer-based EHR modeling. ATHENA uses a weight-sharing supernet that is pretrained once per hospital, allowing candidate architectures to be instantiated as inherited subnetworks and evaluated through fine-tuning rather than independent pretraining. It incorporates a two-layer cross-hospital architecture prior. The first layer retrieves high-performing architecture examples from source sites based on task descriptors, while the second estimates the effects of architectural components using SHapley Additive exPlanations (SHAP)-based meta-regression. These priors guide a multi-agent LLM search using validation feedback from the target hospital. Across six clinical prediction tasks evaluated at one held-out OneFlorida+ site and one external MIMIC-IV site, ATHENA significantly outperforms all four baselines in 9 of 12 site-task evaluations under a strict equal-compute comparison. Using a common pretrained AutoFormer supernet for candidate evaluation, ATHENA ranks first in 9 of 12 evaluations at a search budget of 30. It also shows more consistent architecture selection across repeated searches. ATHENA provides a practical approach for reducing manual architecture tuning in Transformer-based EHR modeling.
