融合电子病历与可穿戴数据，构建连续时间健康表征模型。
Learning Longitudinal Health Representations from EHR and Wearable Data
- 设计联合编码器与共享时序主干，实现多模态数据协同建模。
- 在长期预测与缺损数据下，性能优于单一数据源基线。
- 适合临床风险预测、长期健康监测等场景使用。
基于电子健康记录（EHR）训练的基础模型在多种临床预测任务中表现优异，但受限于文档稀疏且不规则。可穿戴设备提供密集连续的生理信号，却缺乏语义基础。现有方法通常分别建模或通过后期融合结合两类数据。本文提出一种多模态基础模型，将EHR与可穿戴数据共同表示为连续时间潜在过程。模型采用模态专用编码器和共享时序主干，通过自监督与跨模态目标预训练，生成时序一致且临床语义明确的表征。在生理预测与风险建模任务中，该模型在长时序预测及缺失数据条件下均显著优于仅使用EHR或仅使用可穿戴数据的基线模型。结果表明，联合EHR与可穿戴数据预训练能生成更忠实的纵向健康表征。
原文摘要 · Abstract (English)
Foundation models trained on electronic health records show strong performance on many clinical prediction tasks but are limited by sparse and irregular documentation. Wearable devices provide dense continuous physiological signals but lack semantic grounding. Existing methods usually model these data sources separately or combine them through late fusion. We propose a multimodal foundation model that jointly represents electronic health records and wearable data as a continuous time latent process. The model uses modality specific encoders and a shared temporal backbone pretrained with self supervised and cross modal objectives. This design produces representations that are temporally coherent and clinically grounded. Across forecasting physiological and risk modeling tasks the model outperforms strong electronic health record only and wearable only baselines especially at long horizons and under missing data. These results show that joint electronic health record and wearable pretraining yields more faithful representations of longitudinal health.
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