9 papers
Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction
Siqi Li, Chuan Hong, Ziye Tian +9
Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target…
Maximin Learning of Individualized Treatment Effect on Multi-Domain Outcomes
Yuying Lu, Wenbo Fei, Yuanjia Wang +1
Precision mental health requires treatment decisions that account for heterogeneous symptoms reflecting multiple clinical domains. However, existing methods for estimating individu…
Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI
Xin Xiong, Zijian Guo, Haobo Zhu +4
Clinical AI systems frequently suffer performance decay post-deployment due to temporal data shifts, such as evolving populations, diagnostic coding updates (e.g., ICD-9 to ICD-10)…
Model-X Change-Point Detection of Conditional Distribution
Zhuofan Dong, Yiwen Huang, Yan Dong +5
The dynamic nature of many real-world systems can lead to temporal outcome model shifts, causing a deterioration in model accuracy and reliability over time. This requires change-p…
TRACER: Transfer Learning based Real-time Adaptation for Clinical Evolving Risk
Mengying Yan, Ziye Tian, Siqi Li +4
Clinical decision support tools built on electronic health records often experience performance drift due to temporal population shifts, particularly when changes in the clinical e…
RELEAP: Reinforcement-Enhanced Label-Efficient Active Phenotyping for Electronic Health Records
Yang Yang, Kathryn I. Pollak, Bibhas Chakraborty +3
Objective: Electronic health record (EHR) phenotyping often relies on noisy proxy labels, which undermine the reliability of downstream risk prediction. Active learning can reduce…