AI RESEARCH
Multi-lingual Multi-institutional Electronic Health Record based Predictive Model
arXiv CS.LG
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ArXi:2604.00027v1 Announce Type: cross Large-scale EHR prediction across institutions is hindered by substantial heterogeneity in schemas and code systems. Although Common Data Models (CDMs) can standardize records for multi-institutional learning, the manual harmonization and vocabulary mapping are costly and difficult to scale. Text-based harmonization provides an alternative by converting raw EHR into a unified textual form, enabling pooled learning without explicit standardization. However, applying this paradigm to multi-national datasets