This paper proposes a new natural language processing (NLP) application for
identifying medical jargon terms potentially difficult for patients to
comprehend from electronic health record (EHR) notes. We first present a novel
and publicly available dataset with expert-annotated medical jargon terms from
18K+ EHR note sentences ($MedJ$). Then, we introduce a novel medical jargon
extraction ($MedJEx$) model which has been shown to outperform existing
state-of-the-art NLP models. First, MedJEx improved the overall performance
when it was trained on an auxiliary Wikipedia hyperlink span dataset, where
hyperlink spans provide additional Wikipedia articles to explain the spans (or
terms), and then fine-tuned on the annotated MedJ data. Secondly, we found that
a contextualized masked language model score was beneficial for detecting
domain-specific unfamiliar jargon terms. Moreover, our results show that
training on the auxiliary Wikipedia hyperlink span datasets improved six out of
eight biomedical named entity recognition benchmark datasets. Both MedJ and
MedJEx are publicly available.

本文介绍了一种新的自然语言处理应用，用于从电子病历注释中识别医学术语中的专业词汇，提出了一种新的医学术语提取模型 $MedjEx$，应用了辅助的 Wikipedia 超链接跨度数据集进行训练，并使用该模型提高了生物医学命名实体识别基准数据集的表现。