• Am J Emerg Med · Jul 2021

    Review

    Forty years of emergency medicine research: Uncovering research themes and trends through topic modeling.

    • Thomas Porturas and TaylorR AndrewRADepartment of Emergency Medicine, Yale University School of Medicine, United States. Electronic address: richard.taylor@yale.edu..
    • Yale University School of Medicine, United States.
    • Am J Emerg Med. 2021 Jul 1; 45: 213-220.

    Study ObjectiveTopic identification can facilitate knowledge curation, discover thematic relationships, trends, and predict future direction. We aimed to determine through an unsupervised, machine learning approach to topic modeling the most common research themes in emergency medicine over the last 40 years and summarize their trends and characteristics.MethodsWe retrieved the complete reference entries including article abstracts from Ovid for all original research articles from 1980 to 2019 within emergency medicine for six widely-cited journals. Abstracts were processed through a natural language pipeline and analyzed by a latent Dirichlet allocation topic modeling algorithm for unsupervised topic discovery. Topics were further examined through trend analysis, word associations, co-occurrence metrics, and two-dimensional embeddings.ResultsWe retrieved 47,158 articles during the defined time period that were filtered to 20,528 articles for further analysis. Forty topics covering methodologic and clinical areas were discovered. These topics separated into distinct clusters when embedded in two-dimensional space and exhibited consistent patterns of interaction. We observed the greatest increase in popularity in research themes involving risk factors (0.4% to 5.2%), health utilization (1.2% to 5.0%), and ultrasound (0.7% to 3.3%), and a relative decline in research involving basic science (8.9% to 1.1%), cardiac arrest (6.5% to 2.2%), and vitals (6.3% to 1.3%) over the past 40 years. Our data show only very modest growth in mental health and substance abuse research (1.0% to 1.6%), despite ongoing crises.ConclusionsTopic modeling via unsupervised machine learning applied to emergency medicine abstracts discovered coherent topics, trends, and patterns of interaction.Copyright © 2020 Elsevier Inc. All rights reserved.

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