弥合EHR鸿沟：用于跨国医疗表征迁移的非对称对比学习

中文摘要 AI 辅助

跨系统迁移纵向电子健康记录（EHR）表征具有挑战性，因为临床编码、患者群体和医疗工作流程在不同机构和国家的差异显著。我们引入了非对称监督对比学习（Asymmetric SupCon），这是一种受负性临床结果异质性启发的任务特定预训练目标。该目标将共享目标阳性结果的患者聚类，而不明确地将阴性轨迹相互吸引。我们在台湾国民健康保险研究数据库（NHIRD）中398万患者的纵向记录上预训练时间Transformer编码器，并将其迁移到两个美国EHR数据集MIMIC-IV和EHRSHOT。一种结合直接映射与基于嵌入检索的混合语义映射管道实现了跨异质临床词汇表的迁移。在MIMIC-IV上，NHIRD预训练持续优于随机初始化，同时显著缩小了与任务特定域内预训练的性能差距。在EHRSHOT上，迁移模型在事件性疾病预测中表现出特别强的少样本性能。一项受控目标消融实验表明，Asymmetric SupCon在四项评估任务中的三项上取得了最佳AUPRC，在第四项上比标准SupCon低0.003 AUPRC。这些结果支持非对称对比预训练作为任务特定跨国EHR表征迁移的有效方法。代码可在以下网址获取：https://this URL。

英文摘要

Cross-system transfer of longitudinal Electronic Health Record (EHR) representations is challenging because clinical coding, patient populations, and healthcare workflows differ substantially across institutions and countries. We introduce Asymmetric Supervised Contrastive Learning (Asymmetric SupCon), a task-specific pre-training objective motivated by the heterogeneity of negative clinical outcomes. The objective clusters patients sharing a target positive outcome without explicitly attracting negative trajectories toward one another. We pre-train temporal Transformer encoders on longitudinal records from 3.98 million patients in the Taiwanese National Health Insurance Research Database (NHIRD) and transfer them to two U.S. EHR datasets, MIMIC-IV and EHRSHOT. A hybrid semantic mapping pipeline combining direct mappings with embedding-based retrieval enables transfer across heterogeneous clinical vocabularies. On MIMIC-IV, NHIRD pre-training consistently improves over random initialization while substantially narrowing the performance gap to task-specific in-domain pre-training. On EHRSHOT, the transferred models show particularly strong few-shot performance for incident disease prediction. A controlled objective ablation under a matched pre-training scale shows that Asymmetric SupCon achieves higher mean AUPRC than direct supervised BCE transfer on all four evaluated tasks and Standard SupCon on three of four, with a 0.003 AUPRC deficit on readmission. These results support asymmetric contrastive pre-training as an effective approach for task-specific cross-national EHR representation transfer. Code is available at https://github.com/qingYzhang/Asymmetric_SupCon.

发表机构

• Carnegie Mellon University（卡内基梅隆大学）

机构由 AI 辅助整理，请以论文原文为准。