Bridging the EHR Divide: Asymmetric Contrastive Learning for Cross-National Medical Representation Transfer
Cross-system transfer of longitudinal Electronic Health Record (EHR) representations is challenging because clinical coding, patient populations, and healthcare workflows differ substantially across institutions and countries. We introduce Asymmetric Supervised Contrastive Learning (Asymmetric SupCon), a task-specific pre-training objective motivated by the heterogeneity of negative clinical outcomes. The objective clusters patients sharing a target positive outcome without explicitly attracting negative trajectories toward one another. We pre-train temporal Transformer encoders on longitudinal records from 3.98 million patients in the Taiwanese National Health Insurance Research Database...
| Authors | Qingyang Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 4 October 2026 |
| Last updated at source | 6 October 2026 |
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