多语言多机构电子健康记录基于预测模型
Multi-lingual Multi-institutional Electronic Health Record based Predictive Model
AI总结：
本文提出多语言多机构学习方法，通过文本对齐实现跨国ICU数据集的联合训练，优于多语言编码器和翻译方法，提升临床预测性能。
摘要：
大规模跨机构EHR预测受限于模式和代码系统的异质性。尽管Common Data Models (CDMs) 可标准化多机构学习记录，但手动和谐化和词汇映射成本高且难以扩展。基于文本的和谐化提供替代方案，通过将原始EHR转换为统一文本形式，实现池化学习而无需显式标准化。然而，将其应用于多国数据集引入额外异质性，即'语言'，必须解决以实现真正可扩展的EHR学习。本文研究多语言多机构学习用于EHR预测，旨在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。我们比较两种处理语言障碍的策略：(i) 直接建模多语言记录使用多语言编码器，和(ii) 通过LLM基于词级翻译将非英语记录翻译成英语。在七个公共ICU数据集中，十个临床任务和多个预测窗口，翻译基于的语义对齐比多语言编码器产生更可靠的跨数据集性能。多机构学习模型一致优于需要手动特征选择和和谐化的强大基线，并且超过单数据集训练。我们进一步证明，基于文本的框架通过少样本微调有效执行迁移学习，并有额外收益。据我们所知，这是首次将多语言跨国ICU EHR数据集聚合到一个预测模型中，提供了一条可扩展的路径，朝着语言无关的临床预测和未来全球多机构EHR研究迈进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
