From Text to Insight: A Natural Language Processing-Based Analysis of Topics and Trends in Neurosurgery

Abstract

BACKGROUND AND OBJECTIVES: 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: 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: 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: 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.

Commentary: From Text to Insight: A Natural Language Processing–Based Analysis of Topics and Trends in Neurosurgery

“…Nevertheless, despite the rigorous methodology used in this study 1 and the significant results reported by the authors, the study depended on the data extracted from a single journal, which does not reflect the global neurosurgical literature. As mentioned by the authors, many other limitations are existing, including the use of linear models in data analysis, the potential metadata inconsistency, and the lack of other comprehensive validation studies.…”

“…In this review article, 1 Karabacak et al used a new topic-modeling methodology in evaluating the dynamic nature of neurosurgical research within the domain of the Neurosurgery journal, based on its position at the forefront of the neurosurgical fraternity journals. They used the bidirectional encoder representations and transformers (BERT) method, an advanced Natural Language Processing (NLP)–based topic modeling approach.…”

“…In the present review article, 1 the authors used the BERTopic method, which is a topic-modeling approach based on the BERT language model. Topic representations are generated in 3 steps; each document is first transformed using a language model into its embedding representation.…”

“…This study 1 used a two-part methodology to extract and classify the original articles published in the top journal of our specialty. The first part used bibliometric data collection to search for original articles published between 1977 and 2023.…”