用真实临床轨迹训练的模型，可精准预测肥胖人群心血管风险。
Patient foundation model for risk stratification in low-risk overweight patients
- 基于超50万患者病程数据，融合诊断、检验与用药信息建模。
- 在低风险肥胖者中准确预测肥胖相关并发症，且能泛化至未显式建模事件。
- 比体重指数更优，适合用于医保成本分层和预防性医疗决策。
在超重或肥胖患者中实现精准风险分层，对指导预防性护理及分配高成本疗法（如GLP-1受体激动剂）至关重要。我们提出PatientTPP，一种基于超过50万例真实世界临床轨迹训练的神经时间点过程（TPP）模型，通过序列化的诊断、检验与药物记录学习患者表征。该模型扩展了现有TPP方法，纳入静态与数值特征，并结合临床知识进行事件编码。其生成的表征支持下游预测任务，包括在低风险个体中分类肥胖相关结局，即使这些事件未在训练中显式建模。健康经济学评估显示，PatientTPP在按未来心血管相关医疗支出分层患者方面优于体质指数（BMI），更高效识别高风险人群。通过同时建模临床事件的类型与发生时间，PatientTPP提供了一种可解释、通用性强的患者风险建模基础，可直接应用于肥胖相关照护与成本靶向。
原文摘要 · Abstract (English)
Accurate risk stratification in patients with overweight or obesity is critical for guiding preventive care and allocating high-cost therapies such as GLP-1 receptor agonists. We present PatientTPP, a neural temporal point process (TPP) model trained on over 500,000 real-world clinical trajectories to learn patient representations from sequences of diagnoses, labs, and medications. We extend existing TPP modeling approaches to include static and numeric features and incorporate clinical knowledge for event encoding. PatientTPP representations support downstream prediction tasks, including classification of obesity-associated outcomes in low-risk individuals, even for events not explicitly modeled during training. In health economic evaluation, PatientTPP outperformed body mass index in stratifying patients by future cardiovascular-related healthcare costs, identifying higher-risk patients more efficiently. By modeling both the type and timing of clinical events, PatientTPP offers an interpretable, general-purpose foundation for patient risk modeling with direct applications to obesity-related care and cost targeting.
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