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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score.

Sunjae Kwon1, Zonghai Yao1, Harmon S Jordan2

  • 1UMass Amherst.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
|April 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces MedJEx, a new NLP model to identify difficult medical jargon in electronic health records (EHRs). The MedJEx model and its dataset, MedJ, are publicly available to improve patient understanding.

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Area of Science:

  • Natural Language Processing
  • Medical Informatics
  • Computational Linguistics

Background:

  • Electronic Health Records (EHRs) contain complex medical jargon that can hinder patient comprehension.
  • Identifying and simplifying this jargon is crucial for effective patient-provider communication and health literacy.

Purpose of the Study:

  • To develop and evaluate a novel Natural Language Processing (NLP) application, MedJEx, for identifying potentially difficult medical jargon within EHR notes.
  • To introduce MedJ, a new, publicly available dataset of expert-annotated medical jargon from EHR notes.

Main Methods:

  • Creation of the MedJ dataset comprising expert-annotated medical jargon from over 18,000 EHR note sentences.
  • Development of the MedJEx model, incorporating training on an auxiliary Wikipedia hyperlink span dataset and fine-tuning on MedJ data.
  • Utilizing a contextualized masked language model score to enhance jargon detection.

Main Results:

  • The MedJEx model demonstrated superior performance compared to existing state-of-the-art NLP models in medical jargon extraction.
  • Training on auxiliary Wikipedia hyperlink span datasets improved the performance of MedJEx and positively impacted six out of eight biomedical named entity recognition benchmark datasets.
  • A contextualized masked language model score proved beneficial for identifying domain-specific unfamiliar jargon terms.

Conclusions:

  • The developed MedJEx model and MedJ dataset offer a significant advancement in automatically identifying patient-incomprehensible medical jargon from EHRs.
  • Public availability of both MedJ and MedJEx facilitates further research and development in clinical NLP and patient communication tools.
  • The findings highlight the effectiveness of leveraging external knowledge sources like Wikipedia for enhancing specialized NLP tasks.