发表机构
Harvard T.H. Chan School of Public Health; Center for Data Science, Zhejiang University; Harvard Medical School（哈佛陈曾熙公共卫生学院; 浙江大学数据科学中心; 哈佛医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GUARD提出一个统一统计框架，利用有限标记目标数据和多个异质源人群更新部署模型，通过半监督对抗优化和双机器学习实现鲁棒多源迁移，在类风湿关节炎EHR预测中仅用少量标记记录即保持多年准确。
AI 中文摘要
作为临床决策支持工具部署的人工智能模型，由于协变量偏移、概念漂移和跨系统异质性，其性能常常随时间推移和机构间转移而大幅下降。完全重新训练复杂模型通常不可行，尤其是在电子健康记录（EHR）环境中，标记结果数据稀缺且监管限制约束模型修改。我们提出GUARD（引导且不确定性感知的域偏移鲁棒性），这是一个统一的统计框架，利用有限的标记目标数据和多个异质源人群来更新现有部署模型，同时防范未来的分布偏移。GUARD将鲁棒多源迁移学习形式化为一个以当前模型为锚点的半监督对抗优化问题，并采用双机器学习与交叉拟合来获得去偏、高效的估计，利用丰富的未标记目标协变量。随后，它构建了一个不确定性感知的引导式组DRO估计器，结合源和目标信息，同时考虑源模型中采样变异性。我们的框架联合提供了对域偏移的原则性鲁棒性、高效的半监督估计以及临床预测模型多源迁移的有效统计推断。我们通过广泛的模拟实验和一项真实世界应用展示了GUARD的实用性，该应用利用跨越十多年的EHR数据预测类风湿关节炎的未来疾病活动。GUARD仅使用每年少量标记记录即可重新校准预训练模型，并在多年时间范围内保持准确，而仅目标模型和标准迁移估计器在此范围内性能急剧下降。
英文摘要
Artificial intelligence models deployed as clinical decision support tools often suffer substantial performance degradation over time and across institutions due to covariate shift, concept drift, and cross-system heterogeneity. Fully retraining complex models is frequently infeasible, particularly in EHR settings where labeled outcome data are scarce and regulatory constraints limit model modification. We propose GUARD (Guided and Uncertainty-Aware Robustness to Domain shift), a unified statistical framework for updating an existing deployed model using limited labeled target data and multiple heterogeneous source populations while guarding against future distributional shifts. GUARD formulates robust multi-source transfer learning as a semi-supervised, adversarial optimization problem anchored at the current model, and employs double machine learning with cross-fitting to obtain debiased, efficient estimates that leverage abundant unlabeled target covariates. It then constructs an uncertainty-aware, guided group DRO estimator that combines source and target information while accounting for sampling variability in the source models. Our framework jointly provides principled robustness to domain shift, efficient semi-supervised estimation, and valid statistical inference for multi-source transfer of clinical prediction models. We demonstrate the utility of GUARD via extensive simulation experiments and a real world application to predicting future disease activity in rheumatoid arthritis from EHR data spanning more than a decade. GUARD recalibrates a pretrained model with only a small number of labeled records per year and remains accurate over multi-year horizons where target-only and standard transfer estimators degrade sharply.
