Bridging the EHR Divide: Asymmetric Contrastive Learning for Cross-National Medical Representation Transfer

Excerpt

arXiv:2610.04946v2 Announce Type: replace Abstract: 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 t