Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such schemes require expensive human supervision. This study aims to construct a trainable system that can automate the process of semantic interinstitutional code mapping.

中文翻译：

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OMOP-CDM 中基于深度学习的自动术语映射

由于机构间词汇的多样性，很难从多个机构访问医疗数据。已提出标准化方案，例如通用数据模型，作为该问题的解决方案，但此类方案需要昂贵的人工监督。本研究旨在构建一个可训练的系统，该系统可以自动化语义机构间代码映射的过程。