用万例患者数据训练，预测疾病发展轨迹更准。
DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-world Health System
- 基于多医院真实病历构建通用模型，捕捉长期健康变化。
- 在896类疾病中平均准确率AUC达0.871，全类别超0.5。
- 适合临床决策支持、早期干预系统研发者参考。
精准预测疾病发展轨迹对早期干预、资源调配和改善长期预后至关重要。电子健康记录（EHR）为临床环境中的患者健康提供了丰富的纵向视图，但基于精选研究队列训练的模型难以反映实际部署场景，而单一医院数据集则仅能捕获患者轨迹的片段。因此，利用大规模、多医院健康系统进行训练与验证，对于模拟真实临床复杂性尤为关键。本文构建了DT-Transformer，一个在马萨诸塞总医院布里格姆（MGB）系统上训练的基础模型，涵盖170万名患者、5710万条结构化EHR数据，覆盖11家医院及广泛的门诊网络。该模型在留出数据和前瞻性验证中均表现优异：在896种疾病类别中，次事件预测的中位年龄与性别分层AUC达到0.871，所有类别均超过0.5。结果表明，健康系统级训练是打造适用于真实临床预测基础模型的有效路径。
原文摘要 · Abstract (English)
Accurate disease trajectory prediction is critical for early intervention, resource allocation, and improving long-term outcomes. While electronic health records (EHRs) provide a rich longitudinal view of patient health in clinical environments, models trained on curated research cohorts may not reflect routine deployment settings, and those trained on single-hospital datasets capture only fragments of each patient's trajectory. This highlights the importance of leveraging large, multi-hospital health systems for training and validation to better reflect real-world clinical complexity. In this work, we develop DT-Transformer, a foundation model trained on 57.1M structured EHR entries over 1.7M patients from Mass General Brigham (MGB), spanning 11 hospitals and a broad network of outpatient clinics. DT-Transformer achieves strong discrimination in both held-out and prospective validation settings. Next-event prediction achieves a median age- and sex-stratified AUC of 0.871 across 896 disease categories, with all categories exceeding AUC 0.5. These results support health system-scale training as a path toward foundation models suited to real-world clinical forecasting.
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