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This paper proposes a new natural language processing (NLP) application foridentifying medical jargon terms potentially difficult for patients tocomprehend from electronic health record (EHR) notes. We first present a noveland publicly available dataset with expert-annotated medical jargon terms from18K+ EHR note sentences ($MedJ$). Then, we introduce a novel medical jargonextraction ($MedJEx$) model which has been shown to outperform existingstate-of-the-art NLP models. First, MedJEx improved the overall performancewhen it was trained on an auxiliary Wikipedia hyperlink span dataset, wherehyperlink spans provide additional Wikipedia articles to explain the spans (orterms), and then fine-tuned on the annotated MedJ data. Secondly, we found thata contextualized masked language model score was beneficial for detectingdomain-specific unfamiliar jargon terms. Moreover, our results show thattraining on the auxiliary Wikipedia hyperlink span datasets improved six out ofeight biomedical named entity recognition benchmark datasets. Both MedJ andMedJEx are publicly available.

MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score