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

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 expertannotated medical jargon terms from 18K+ EHR note sentences (M edJ). Then, we introduce a novel medical jargon extraction (M edJEx) 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. MedJEx is publicly available 1 . UMLS QuickUMLS Weighted Score Feature Binary Feature Wiki_trained LM Tokenizer CRF Layer MLP MLP MLP Binary Feature Extraction Biomedical Concepts Term Weighting Initialize with trained weights WikiHyperlink Training Auxiliary Feature Extraction Target Model Input Hidden Weighted emission Emission Final emission WordFreq … exacerbated by his shock liver … … 'exacerbated by' 'shock' 'liver'