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stat.ME2026

Domain Adaptation Targeting Heterogeneous and Imbalanced Subgroups

Doudou Zhou, Mengyan Li, Yun Wang +2

Domain adaptation enables generalizable and efficient data-driven research. However, existing work has largely focused on domain adaptation for some intrinsically homogeneous targe…

stat.ME2026

Spherical Mixture Integration for Latent Embedding Alignment across Multi-Source Feature Spaces

Yuming Zhang, Congyuan Duan, Dong Xia +2

Multi-institutional electronic health record (Multi-EHR) data have emerged as a powerful resource for developing predictive models to support clinical decisions and for generating…

stat.ME2026

Structured Transfer Learning for Survival Risk Stratification in Data-Sparse Clinical Cohorts

Junhan Yu, Yurui Chen, Juan Delgado-SanMartin +3

Background: Survival prediction models are often less reliable in clinical groups with limited sample sizes or few outcome events. Target-only models may be unstable, whereas model…

stat.ME2026

Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processes

Mingyuan Xu, Zongqi Xia, Tianxi Cai +2

We often collect data from multiple sites (e.g., hospitals) that share common structure but also exhibit heterogeneity. This paper aims to learn robust sequential decision-making p…

stat.ME20251 cited

Inference of Dependency Knowledge Graph for Electronic Health Records

Zhiwei Xu, Ziming Gan, Doudou Zhou +3

The effective analysis of high-dimensional Electronic Health Record (EHR) data, with substantial potential for healthcare research, presents notable methodological challenges. Empl…

stat.ME2025

Latent Factor Point Processes for Patient Representation in Electronic Health Records

Parker Knight, Doudou Zhou, Zongqi Xia +2

Electronic health records (EHR) contain valuable longitudinal patient-level information, yet most statistical methods reduce the irregular timing of EHR codes into simple counts, t…