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

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11733 - 11751 December 7-11, 2022 ©2022 Association for Computational Linguistics MedJEx: A Medical Jargon Extraction Model with Wiki’s Hyperlink Span and Contextualized Masked Language Model Score Sunjae Kwon1, Zonghai Yao1, Harmon S. Jordan2, David A. Levy3, Brian Corner2, Hong Yu1,3,4,5 1UMass Amherst, 2Health Research Consultant, 3UMass Lowell, 4UMass Medical School, 5U.S. Department of Veterans Affairs sunjaekwon@umass.edu, zonghaiyao@umass.edu, harmon.s.jordan@gmail.com, david_levy@uml.edu, brian.corner@umassmed.edu, hong_yu@uml.edu Abstract 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 mod- els. First, MedJEx improved the overall per- formance when it was trained on an auxiliary Wikipedia hyperlink span dataset, where hyper- link spans provide additional Wikipedia arti- cles to explain the spans (or terms), and then fine-tuned on the annotated MedJ data. Sec- ondly, we found that a contextualized masked language model score was beneficial for detect- ing 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 en- tity recognition benchmark datasets. MedJEx is publicly available 1. 1 Introduction Allowing patients to access their electronic health records (EHRs) represents a new and personal- ized communication channel that has the poten- tial to improve patient involvement in care and assist communication between physicians, patients, and other healthcare providers (Baldry et al., 1986; Schillinger et al., 2009). However, studies showed that patients do not understand medical jargon in their EHR notes (Chen et al., 2018). To improve patients’ EHR note comprehension, it is important to identify medical jargon terms that are difficult for patients to understand. Un- like the traditional concept identification or named 1https://github.com/MozziTasteBitter/ MedJEx entity recognition (NER) tasks, where the tasks mainly center on semantic salient entities, detect- ing such medical jargon terms takes into consider- ation the perspective of user comprehension. Tra- ditional NER approaches such as using compre- hensive clinical terminological resources (e.g., the Unified Medical Language System (UMLS) (Bo- denreider, 2004)) would identify terms such as "wa- ter" and "fat", which are not considered difficult for patients to comprehend. Meanwhile, using term fre- quency (TF) as the proxy for medical jargon term identification will miss outliers such as "shock," which is a term frequently used in the open do- main with its common sense: "a sudden upsetting or surprising event or experience." However, EHR notes incorporate its uncommon sense: "a medi- cal condition caused by severe injury, pain, loss of blood, or fear that slows down the flow of blood." (Shock, 2022). Thus, "shock" should be identified as a jargon term from EHR notes since it would be difficult for patients to comprehend, even though its TF is …

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