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Updated: May 24, 2026

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Topic Modeling and Trend Analysis in Medical Education Literature Using Projective Non-Negative Matrix Factorization.

Emir Karayağiz1, Beyzanur Siyah1, Yunus Emre Kayaoğlu1

  • 1Karadeniz Technical University, Department of Computer Sciences, Faculty of Science Trabzon, Turkey.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study analyzed 15 years of medical education research using Non-Negative Matrix Factorization (NMF). Key trends include the rise of Generative AI and Large Language Models (LLMs) and increased focus on equity.

Keywords:
Medical EducationNon-Negative Matrix FactorizationPubMedTopic Modelingn-gram

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Area of Science:

  • Medical Education Research
  • Computational Linguistics
  • Data Science in Academia

Background:

  • Analyzing trends in medical education literature is crucial for understanding research evolution.
  • Traditional methods may not capture the nuances of large-scale textual data effectively.

Purpose of the Study:

  • To identify and analyze key topic trends in medical education research over the past 15 years.
  • To leverage advanced computational techniques for a comprehensive literature review.

Main Methods:

  • Utilized Non-Negative Matrix Factorization (NMF) and Projective Non-Negative Matrix Factorization (PNMF) on 46,292 PubMed abstracts.
  • Employed a pre-processing pipeline including Unicode normalization, text cleaning, stop-word removal, and n-gram generation.
  • Generated topic names using Claude Artificial Intelligence (AI) and visualized trends with t-distributed Stochastic Neighbor Embedding (t-SNE).

Main Results:

  • Identified 10 key topics within medical education literature.
  • Revealed a significant recent emergence of Generative AI and Large Language Models (LLMs).
  • Observed a notable increase in studies focusing on equity in recent years.

Conclusions:

  • NMF provides a robust method for uncovering thematic shifts in academic literature.
  • The medical education field is rapidly integrating discussions on artificial intelligence and equity.