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.
Abstract (translated)
跨机构的大规模电子健康记录预测因数据模式和编码系统的显著异构性而受阻。尽管通用数据模型可标准化记录以支持多机构学习，但手动数据 harmonization 与术语映射成本高昂且难以规模化。基于文本的 harmonization 提供了一种替代方案：通过将原始电子健康记录转换为统一的文本形式，无需显式标准化即可实现数据聚合学习。然而，将该范式应用于跨国数据集会引入额外的异构性层——即“语言”，这是实现真正可扩展的电子健康记录学习必须解决的问题。本研究探索电子健康记录预测的多语言多机构学习，旨在无需手动标准化的情况下实现跨国ICU数据集的联合训练。我们比较了两种处理语言障碍的实用策略：(i) 使用多语言编码器直接建模多语言记录；(ii) 通过基于大语言模型的词级翻译将非英语记录转换为英语。在七个公共ICU数据集、十个临床任务及多种预测时间窗口的实验中，基于翻译的语言对齐策略比多语言编码器产生了更可靠的跨数据集性能。该多机构学习模型持续优于需要手动特征选择与 harmonization 的强基线方法，并超越了单数据集训练结果。我们进一步证明，结合语言对齐的基于文本框架通过小样本微调能有效实现迁移学习，并取得额外性能提升。据我们所知，这是首个将多语言跨国ICU电子健康记录数据集聚合为单一预测模型的研究，为构建与语言无关的临床预测及未来全球多机构电子健康记录研究提供了可扩展的路径。
URL
https://arxiv.org/abs/2604.00027
