Multi-lingual Multi-institutional Electronic Health Record based Predictive Model

• URL: http://arxiv.org/abs/2604.00027v1
• Date: Wed, 11 Mar 2026 07:21:27 GMT
• Title: Multi-lingual Multi-institutional Electronic Health Record based Predictive Model
• Abstract summary: We show that text-based framework with lingual alignment effectively performs transfer learning via few-shot fine-tuning.<n>This is the first study to aggregate multilingual multinational ICU EHR datasets into one predictive model.
• Score: 13.930489950153657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 introduces an additional layer of heterogeneity, which is "language" that must be addressed for truly scalable EHRs learning. In this work, we investigate multilingual multi-institutional learning for EHR prediction, aiming to enable pooled training across multinational ICU datasets without manual standardization. We compare two practical strategies for handling language barriers: (i) directly modeling multilingual records with multilingual encoders, and (ii) translating non-English records into English via LLM-based word-level translation. Across seven public ICU datasets, ten clinical tasks with multiple prediction windows, translation-based lingual alignment yields more reliable cross-dataset performance than multilingual encoders. The multi-institutional learning model consistently outperforms strong baselines that require manual feature selection and harmonization, and also surpasses single-dataset training. We further demonstrate that text-based framework with lingual alignment effectively performs transfer learning via few-shot fine-tuning, with additional gains. To our knowledge, this is the first study to aggregate multilingual multinational ICU EHR datasets into one predictive model, providing a scalable path toward language-agnostic clinical prediction and future global multi-institutional EHR research.

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