EHRWorld: 一个以患者为中心的医疗世界模型用于长周期临床轨迹
EHRWorld: A Patient-Centric Medical World Model for Long-Horizon Clinical Trajectories
AI总结：
EHRWorld通过因果序列范式训练，有效解决长周期临床模拟中的误差累积问题，提升医疗世界模型的稳定性与可靠性。
摘要：
世界模型提供了一个原理上模拟干预后未来状态的框架，但在复杂、高风险的领域如医学中实现这样的模型仍具有挑战性。最近的大型语言模型（LLMs）在静态医疗推理任务上表现强劲，引发了它们能否作为动态医疗世界模型来模拟疾病进展和治疗结果的问题。在本工作中，我们表明，仅整合医学知识的LLMs在连续干预下难以保持一致的患者状态，导致长周期临床模拟中出现误差累积。为解决这一限制，我们引入了EHRWorld，一个以患者为中心的医疗世界模型，其在因果序列范式下进行训练，同时还有EHRWorld-110K，一个从真实世界电子健康记录中衍生出的大型纵向临床数据集。广泛的评估表明，EHRWorld在很大程度上优于基于LLM的简单基线，实现了更稳定的长周期模拟、对临床敏感事件的建模改进以及有利的推理效率，突显了在因果基础上、时间上演进的临床数据训练对于可靠和稳健的医疗世界建模的必要性。
英文摘要：
World models offer a principled framework for simulating future states under interventions, but realizing such models in complex, high-stakes domains like medicine remains challenging. Recent large language models (LLMs) have achieved strong performance on static medical reasoning tasks, raising the question of whether they can function as dynamic medical world models capable of simulating disease progression and treatment outcomes over time. In this work, we show that LLMs only incorporating medical knowledge struggle to maintain consistent patient states under sequential interventions, leading to error accumulation in long-horizon clinical simulation. To address this limitation, we introduce EHRWorld, a patient-centric medical world model trained under a causal sequential paradigm, together with EHRWorld-110K, a large-scale longitudinal clinical dataset derived from real-world electronic health records. Extensive evaluations demonstrate that EHRWorld significantly outperforms naive LLM-based baselines, achieving more stable long-horizon simulation, improved modeling of clinically sensitive events, and favorable reasoning efficiency, highlighting the necessity of training on causally grounded, temporally evolving clinical data for reliable and robust medical world modeling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
