用真实病历训练的医疗世界模型，能更稳定预测长期治疗效果。
EHRWorld: A Patient-Centric Medical World Model for Long-Horizon Clinical Trajectories
- 基于因果时序框架训练，保持患者状态一致性
- 在11万例病历数据上验证，长周期模拟更稳定
- 适合临床决策支持与治疗方案预演场景
世界模型为干预下的未来状态模拟提供了系统性框架，但在医疗等高风险复杂领域仍面临挑战。尽管大语言模型在静态医学推理任务中表现优异，但仅依赖医学知识的模型在连续干预下难以维持患者状态一致性，导致长周期模拟中误差累积。为此，我们提出EHRWorld——一种以患者为中心的医疗世界模型，采用因果时序训练范式，并构建了EHRWorld-110K这一大规模纵向临床数据集，源自真实电子健康记录。大量评估显示，EHRWorld显著优于基线方法，在长周期模拟中表现更稳定，对临床敏感事件建模更准确，且推理效率更优，表明基于因果驱动、时间演化的临床数据训练是实现可靠医疗世界模型的关键。
原文摘要 · Abstract (English)
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.
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