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

arXiv: https://arxiv.org/abs/2210.05875 · HTML: https://mlsanity.com/paper/2210.05875/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 71 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Factual Probing Is [MASK]: Learning vs. Learning to Recall](https://mlsanity.com/paper/2104.05240/) (2021)
- [Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit](https://mlsanity.com/paper/2010.01165/) (2020)
- [UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus](https://mlsanity.com/paper/2010.10391/) (2020)
- [Design Challenges and Misconceptions in Neural Sequence Labeling](https://mlsanity.com/paper/1806.04470/) (2018)
- [A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networks](https://mlsanity.com/paper/2007.00800/) (2020)
- [MedCAT -- Medical Concept Annotation Tool](https://mlsanity.com/paper/1912.10166/) (2019)
- [Analyzing Wrap-Up Effects through an Information-Theoretic Lens](https://mlsanity.com/paper/2203.17213/) (2022)
- [Why Do Masked Neural Language Models Still Need Common Sense Knowledge?](https://mlsanity.com/paper/1911.03024/) (2019)
- [Self-Diagnosis through AI-enabled Chatbot-based Symptom Checkers: User Experiences and Design Considerations](https://mlsanity.com/paper/2101.04796/) (2021)
- [Unsupervised Ensemble Ranking of Terms in Electronic Health Record Notes Based on Their Importance to Patients](https://mlsanity.com/paper/1703.00538/) (2017)

## Built on by (most cited first, 20 of 20)

- [ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes](https://mlsanity.com/paper/2403.05795/) (2024)
- [PaniniQA: Enhancing Patient Education Through Interactive Question Answering](https://mlsanity.com/paper/2308.03253/) (2023)
- [README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP](https://mlsanity.com/paper/2312.15561/) (2023)
- [Context Variance Evaluation of Pretrained Language Models for Prompt-based Biomedical Knowledge Probing](https://mlsanity.com/paper/2211.10265/) (2022)
- [UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?](https://mlsanity.com/paper/2306.16931/) (2023)
- [From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?](https://mlsanity.com/paper/2403.11894/) (2024)
- [ReadCtrl: Personalizing text generation with readability-controlled instruction learning](https://mlsanity.com/paper/2406.09205/) (2024)
- [Improving Summarization with Human Edits](https://mlsanity.com/paper/2310.05857/) (2023)
- [Automated Identification of Eviction Status from Electronic Health Record Notes](https://mlsanity.com/paper/2212.02762/) (2022)
- [Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information](https://mlsanity.com/paper/2305.01788/) (2023)
- [MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning](https://mlsanity.com/paper/2507.07419/) (2025)
- [Chatbot To Help Patients Understand Their Health](https://mlsanity.com/paper/2509.05818/) (2025)
- [ODD: A Benchmark Dataset for the Natural Language Processing based Opioid Related Aberrant Behavior Detection](https://mlsanity.com/paper/2307.02591/) (2023)
- [An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning](https://mlsanity.com/paper/2211.14539/) (2022)
- [CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports](https://mlsanity.com/paper/2310.16095/) (2023)
- [EHR Interaction Between Patients and AI: NoteAid EHR Interaction](https://mlsanity.com/paper/2312.17475/) (2023)
- [Blocks Architecture (BloArk): Efficient, Cost-Effective, and Incremental Dataset Architecture for Wikipedia Revision History](https://mlsanity.com/paper/2410.04410/) (2024)
- [Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Study](https://mlsanity.com/paper/2502.16022/) (2025)
- [Rethinking Patient Education as Multi-turn Multi-modal Interaction](https://mlsanity.com/paper/2604.14656/) (2026)
- [Domain-Specific Jargon in Large Language Models: A Comparative Analysis between General-Purpose and Specialist Models](https://mlsanity.com/paper/2609.13556/) (2026)

## Abstract (arXiv)

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.