From Text to Insight: A Natural Language Processing-Based Analysis of Topics and Trends in Neurosurgery.
Journal
Neurosurgery
ISSN: 1524-4040
Titre abrégé: Neurosurgery
Pays: United States
ID NLM: 7802914
Informations de publication
Date de publication:
01 Apr 2024
01 Apr 2024
Historique:
received:
22
08
2023
accepted:
02
10
2023
pubmed:
21
11
2023
medline:
21
11
2023
entrez:
21
11
2023
Statut:
ppublish
Résumé
Neurosurgical research is a rapidly evolving field, with new research topics emerging continually. To provide a clearer understanding of the evolving research landscape, our study aimed to identify and analyze the prevalent research topics and trends in Neurosurgery. We used BERTopic, an advanced natural language processing-based topic modeling approach, to analyze papers published in the journal Neurosurgery . Using this method, topics were identified based on unique sets of keywords that encapsulated the core themes of each article. Linear regression models were then trained on the topic probabilities to identify trends over time, allowing us to identify "hot" (growing in prominence) and "cold" (decreasing in prominence) topics. We also performed a focused analysis of the trends in the current decade. Our analysis led to the categorization of 12 438 documents into 49 distinct topics. The topics covered a wide range of themes, with the most commonly identified topics being "Spinal Neurosurgery" and "Treatment of Cerebral Ischemia." The hottest topics of the current decade were "Peripheral Nerve Surgery," "Unruptured Aneurysms," and "Endovascular Treatments" while the cold topics were "Chiari Malformations," "Thromboembolism Prophylaxis," and "Infections." Our study underscores the dynamic nature of neurosurgical research and the evolving focus of the field. The insights derived from the analysis can guide future research directions, inform policy decisions, and identify emerging areas of interest. The use of natural language processing in synthesizing and analyzing large volumes of academic literature demonstrates the potential of advanced analytical techniques in understanding the research landscape, paving the way for similar analyses across other medical disciplines.
Sections du résumé
BACKGROUND AND OBJECTIVES
OBJECTIVE
Neurosurgical research is a rapidly evolving field, with new research topics emerging continually. To provide a clearer understanding of the evolving research landscape, our study aimed to identify and analyze the prevalent research topics and trends in Neurosurgery.
METHODS
METHODS
We used BERTopic, an advanced natural language processing-based topic modeling approach, to analyze papers published in the journal Neurosurgery . Using this method, topics were identified based on unique sets of keywords that encapsulated the core themes of each article. Linear regression models were then trained on the topic probabilities to identify trends over time, allowing us to identify "hot" (growing in prominence) and "cold" (decreasing in prominence) topics. We also performed a focused analysis of the trends in the current decade.
RESULTS
RESULTS
Our analysis led to the categorization of 12 438 documents into 49 distinct topics. The topics covered a wide range of themes, with the most commonly identified topics being "Spinal Neurosurgery" and "Treatment of Cerebral Ischemia." The hottest topics of the current decade were "Peripheral Nerve Surgery," "Unruptured Aneurysms," and "Endovascular Treatments" while the cold topics were "Chiari Malformations," "Thromboembolism Prophylaxis," and "Infections."
CONCLUSION
CONCLUSIONS
Our study underscores the dynamic nature of neurosurgical research and the evolving focus of the field. The insights derived from the analysis can guide future research directions, inform policy decisions, and identify emerging areas of interest. The use of natural language processing in synthesizing and analyzing large volumes of academic literature demonstrates the potential of advanced analytical techniques in understanding the research landscape, paving the way for similar analyses across other medical disciplines.
Identifiants
pubmed: 37988054
doi: 10.1227/neu.0000000000002763
pii: 00006123-990000000-00970
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
679-689Informations de copyright
Copyright © Congress of Neurological Surgeons 2023. All rights reserved.
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