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

TL;DR

This study explores multilingual multi-institutional EHR prediction, demonstrating that translation-based language alignment outperforms multilingual encoders across diverse ICU datasets, enabling scalable, language-agnostic clinical prediction models.

Contribution

It introduces a scalable, text-based framework for multilingual EHR prediction that surpasses traditional methods requiring manual standardization, facilitating global multi-institutional research.

Findings

Translation-based lingual alignment outperforms multilingual encoders.

The model surpasses baselines with manual feature selection.

Text-based framework enables effective transfer learning with few-shot fine-tuning.

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.