用迁移学习实时适应临床风险模型变化，提升预测准确性。
TRACER: Transfer Learning based Real-time Adaptation for Clinical Evolving Risk
- 通过识别就诊记录的过渡状态，用迁移学习动态调整模型。
- 在新冠疫情期间预测急诊入院，区分度和校准性均优于静态模型。
- 适合需要持续更新的临床预测系统，尤其应对新病或系统变更。
基于电子健康记录构建的临床决策支持工具常因时间性人群变化导致性能下降，尤其在临床环境变化初期仅影响部分患者时，会形成混合人群。此类病例构成变化常见于系统级操作更新或新疾病出现（如新冠）。我们提出TRACER（基于迁移学习的临床风险实时适应框架），通过识别就诊级别的过渡归属，利用迁移学习在不重新训练全模型的前提下自适应调整预测模型。模拟研究表明，TRACER优于基于历史或当前数据训练的静态模型。在真实世界中预测新冠过渡期急诊就诊后住院情况时，TRACER显著提升了模型的区分度与校准性。该方法为在不断演变且异质的临床环境中保持预测性能提供了可扩展方案。
原文摘要 · Abstract (English)
Clinical decision support tools built on electronic health records often experience performance drift due to temporal population shifts, particularly when changes in the clinical environment initially affect only a subset of patients, resulting in a transition to mixed populations. Such case-mix changes commonly arise following system-level operational updates or the emergence of new diseases, such as COVID-19. We propose TRACER (Transfer Learning-based Real-time Adaptation for Clinical Evolving Risk), a framework that identifies encounter-level transition membership and adapts predictive models using transfer learning without full retraining. In simulation studies, TRACER outperformed static models trained on historical or contemporary data. In a real-world application predicting hospital admission following emergency department visits across the COVID-19 transition, TRACER improved both discrimination and calibration. TRACER provides a scalable approach for maintaining robust predictive performance under evolving and heterogeneous clinical conditions.
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