arXiv cs.CY· Yuxin Xiao, Sheng Zhang, Chandan Singh, Tristan Naumann, Hoifung Poon, Jianfeng Gao, Xiaodong Liu·· 10 小时前AI 评分31
用患者轨迹强化学习提升 EHR 基础模型的临床推理能力
Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models
AI 导读
研究者提出一种强化学习微调框架，将 EHR 基础模型视为患者轨迹上的生成策略，把再入院等临床预测问题建模为事件条件化、限定时间窗口的推理任务，并设计时间感知、对 rollout 敏感的奖励。实验显示 RL 微调稳定优于预训练骨干和强基线，能让更小的模型在数据有限场景下追平更大的预训练模型，并实现跨任务正向迁移。
正文 · 原文
Abstract:Electronic health record (EHR) foundation models trained on longitudinal patient trajectories have demonstrated strong performance across diverse clinical prediction tasks. However, their clinical reasoning capabilities remain constrained by next-token prediction on limited and incomplete EHR data. To address this, we propose a reinforcement learning (RL) fine-tuning framework that treats EHR foundation models as generative policies over patient trajectories. We formulate common clinical prediction problems (e.g., hospital readmission) as event-conditioned, time-windowed reasoning tasks. We then design time-aware, rollout-sensitive rewards to account for finite rollout lengths and temporally inconclusive outcomes. We find that RL fine-tuning consistently improves over pre-trained backbones and strong baselines. Notably, it enables smaller models to match larger pre-trained models in data-limited regimes and induces positive transfer across tasks. Further analysis shows that RL fine-tuned models generate trajectories with stronger structural and semantic alignment to ground truth and greater downstream utility.
| Comments: | Accepted by ML4H 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2609.12277 [cs.LG] |
| (or arXiv:2609.12277v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.12277 arXiv-issued DOI via DataCite |
Submission history
From: Yuxin Xiao [view email]
[v1]
Thu, 10 Sep 2026 23:10:38 UTC (202 KB)
[v2]
Tue, 10 Nov 2026 00:24:02 UTC (199 KB)
来源：arXiv cs.CY · arxiv.org