ArXiv Preprint
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.
Sunjae Kwon, Zonghai Yao, Harmon S. Jordan, David A. Levy, Brian Corner, Hong Yu
2022-10-12
