Research topics and hotspot trends of lumbar spondylolisthesis: A text-mining study with machine learning

Mapping the Degenerative Cervical Myelopathy Research Landscape: Topic Modeling of the Literature

Study DesignTopic modeling of literature.ObjectivesOur study has 2 goals: (i) to clarify key themes in degenerative cervical myelopathy (DCM) research, and (ii) to evaluate the current trends in the popularity or decline of these topics. Additionally, we aim to highlight the potential of natural language processing (NLP) in facilitating research syntheses.MethodsDocuments were retrieved from Scopus, preprocessed, and modeled using BERTopic, an NLP-based topic modeling method. We specified a minimum topic size of 25 documents and 50 words per topic. After the models were trained, they generated a list of topics and corresponding representative documents. We utilized linear regression models to examine trends within the identified topics. In this context, topics exhibiting increasing linear slopes were categorized as "hot topics," while those with decreasing slopes were categorized as "cold topics".ResultsOur analysis retrieved 3510 documents that were classified into 21 different topics. The 3 most frequently occurring topics were "OPLL" (ossification of the posterior longitudinal ligament), "Anterior Fusion," and "Surgical Outcomes." Trend analysis revealed the hottest topics of the decade to be "Animal Models," "DCM in the Elderly," and "Posterior Decompression" while "Morphometric Analyses," "Questionnaires," and "MEP and SSEP" were identified as being the coldest topics.ConclusionsOur NLP methodology conducted a thorough and detailed analysis of DCM research, uncovering valuable insights into research trends that were otherwise difficult to discern using traditional techniques. The results provide valuable guidance for future research directions, policy considerations, and identification of emerging trends.

Simplifying synthesis of the expanding glioblastoma literature: a topic modeling approach

PURPOSE

Our study aims to discover the leading topics within glioblastoma (GB) research, and to examine if these topics have "hot" or "cold" trends. Additionally, we aim to showcase the potential of natural language processing (NLP) in facilitating research syntheses, offering an efficient strategy to dissect the landscape of academic literature in the realm of GB research.

METHODS

The Scopus database was queried using "glioblastoma" as the search term, in the "TITLE" and "KEY" fields. BERTopic, an NLP-based topic modeling (TM) method, was used for probabilistic TM. We specified a minimum topic size of 300 documents and 5% probability cutoff for outlier detection. We labeled topics based on keywords and representative documents and visualized them with word clouds. Linear regression models were utilized to identify "hot" and "cold" topic trends per decade.

RESULTS

Our TM analysis categorized 43,329 articles into 15 distinct topics. The most common topics were Genomics, Survival, Drug Delivery, and Imaging, while the least common topics were Surgical Resection, MGMT Methylation, and Exosomes. The hottest topics over the 2020s were Viruses and Oncolytic Therapy, Anticancer Compounds, and Exosomes, while the cold topics were Surgical Resection, Angiogenesis, and Tumor Metabolism.

CONCLUSION

Our NLP methodology provided an extensive analysis of GB literature, revealing valuable insights about historical and contemporary patterns difficult to discern with traditional techniques. The outcomes offer guidance for research directions, policy, and identifying emerging trends. Our approach could be applied across research disciplines to summarize and examine scholarly literature, guiding future exploration.

Tracing topics and trends in drug-resistant epilepsy research using a natural language processing-based topic modeling approach

Epilepsy is a common neurological disorder affecting over 70 million people worldwide. Although many patients achieve seizure control with anti-epileptic drugs (AEDs), 30%-40% develop drug-resistant epilepsy (DRE), where seizures persist despite adequate trials of AEDs. DRE is associated with reduced quality of life, increased mortality and morbidity, and greater socioeconomic challenges. The continued intractability of DRE has fueled exponential growth in research that aims to understand and treat this serious condition. However, synthesizing this vast and continuously expanding DRE literature to derive insights poses considerable difficulties for investigators and clinicians. Conventional review methods are often prolonged, hampering the timely application of findings. More-efficient approaches to analyze the voluminous research are needed. In this study, we utilize a natural language processing (NLP)-based topic modeling approach to examine the DRE publication landscape, uncovering key topics and trends. Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic. This technique employs transformer models like BERT (Bidirectional Encoder Representations from Transformers) for contextual understanding, thereby enabling accurate topic categorization. Analysis revealed 18 distinct topics spanning various DRE research areas. The 10 most common topics, including "AEDs," "Neuromodulation Therapy," and "Genomics," were examined further. "Cannabidiol," "Functional Brain Mapping," and "Autoimmune Encephalitis" emerged as the hottest topics of the current decade, and were examined further. This NLP methodology provided valuable insights into the evolving DRE research landscape, revealing shifting priorities and declining interests. Moreover, we demonstrate an efficient approach to synthesizing and visualizing patterns within extensive literature that could be applied to other research fields.

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

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.

Exploiting Natural Language Processing to Unveil Topics and Trends of Traumatic Brain Injury Research

Traumatic brain injury (TBI) has evolved from a topic of relative obscurity to one of widespread scientific and lay interest. The scope and focus of TBI research have shifted, and research trends have changed in response to public and scientific interest. This study has two primary goals: first, to identify the predominant themes in TBI research; and second, to delineate "hot" and "cold" areas of interest by evaluating the current popularity or decline of these topics. Hot topics may be dwarfed in absolute numbers by other, larger TBI research areas but are rapidly gaining interest. Likewise, cold topics may present opportunities for researchers to revisit unanswered questions. We utilized BERTopic, an advanced natural language processing (NLP)-based technique, to analyze TBI research articles published since 1990. This approach facilitated the identification of key topics by extracting sets of distinctive keywords representative of each article's core themes. Using these topics' probabilities, we trained linear regression models to detect trends over time, recognizing topics that were gaining (hot) or losing (cold) relevance. Additionally, we conducted a specific analysis focusing on the trends observed in TBI research in the current decade (the 2020s). Our topic modeling analysis categorized 42,422 articles into 27 distinct topics. The 10 most frequently occurring topics were: "Rehabilitation," "Molecular Mechanisms of TBI," "Concussion," "Repetitive Head Impacts," "Surgical Interventions," "Biomarkers," "Intracranial Pressure," "Posttraumatic Neurodegeneration," "Chronic Traumatic Encephalopathy," and "Blast Induced TBI," while our trend analysis indicated that the hottest topics of the current decade were "Genomics," "Sex Hormones," and "Diffusion Tensor Imaging," while the cooling topics were "Posttraumatic Sleep," "Sensory Functions," and "Hyperosmolar Therapies." This study highlights the dynamic nature of TBI research and underscores the shifting emphasis within the field. The findings from our analysis can aid in the identification of emerging topics of interest and areas where there is little new research reported. By utilizing NLP to effectively synthesize and analyze an extensive collection of TBI-related scholarly literature, we demonstrate the potential of machine learning techniques in understanding and guiding future research prospects. This approach sets the stage for similar analyses in other medical disciplines, offering profound insights and opportunities for further exploration.

Natural language processing reveals research trends and topics in The Spine Journal over two decades: a topic modeling study

BACKGROUND CONTEXT

The field of spine research is rapidly evolving, with new research topics continually emerging. Analyzing topics and trends in the literature can provide insights into the shifting research landscape.

PURPOSE

This study aimed to elucidate prevalent and emerging research topics and trends within The Spine Journal using a natural language processing technique called topic modeling.

METHODS

We utilized BERTopic, a topic modeling technique rooted in natural language processing (NLP), to examine articles from The Spine Journal. Through this approach, we discerned topics from distinct keyword clusters and representative documents that represented the main concepts of each topic. We then used linear regression models on these topic likelihoods to trace trends over time, pinpointing both "hot" (growing in prominence) and "cold" (decreasing in prominence) topics. Additionally, we conducted an in-depth review of the trending topics in the present decade.

RESULTS

Our analysis led to the categorization of 3358 documents into 30 distinct topics. These topics spanned a wide range of themes, with the most commonly identified topics being "Outcome Measures," "Scoliosis," and "Intradural Lesions." Throughout the history of the journal, the three hottest topics were "Degenerative Cervical Myelopathy," "Osteoporosis," and "Opioid Use." Conversely, the coldest topics were "Intradural Lesions," "Extradural Tumors," and "Vertebral Augmentation." Within the current decade, the hottest topics were "Screw Biomechanics," "Paraspinal Muscles," and "Biologics for Fusion," whereas the cold topics were "Intraoperative Blood Loss," "Construct Biomechanics," and "Material Science."

CONCLUSIONS

This study accentuates the dynamic nature of spine research and the changing focus within the field. The insights gleaned from our analysis can steer future research directions, inform policy decisions, and spotlight emerging areas of interest. The implementation of NLP to synthesize and analyze vast amounts of academic literature exhibits the potential of advanced analytical techniques in comprehending the research landscape, setting a precedent for similar analyses across other medical disciplines.

A Century of Evolution in Spine Surgery Publications: A Bibliometric Analysis of the Field From 1900 to 2023

BACKGROUND AND OBJECTIVES

Spine surgery has advanced in concert with our deeper understanding of its elements. Narrowly focused bibliometric analyses have been conducted previously, but never on the entire corpus of the field. Using big data and bibliometrics, we appraised the entire corpus of spine surgery publications to study the evolution of the specialty as a scholarly field since 1900.

METHODS

We queried Web of Science for all contents from 13 major publications dedicated to spine surgery. We next queried by topic [topic = (spine OR spinal OR vertebrae OR vertebral OR intervertebral OR disc OR disk)]; these results were filtered to include articles published by 49 other publications that were manually determined to contain pertinent articles. Articles, along with their metadata, were exported. Statistical and bibliometric analyses were performed using the Bibliometrix R package and various Python packages.

RESULTS

Eighty-five thousand five hundred articles from 62 journals and 134 707 unique authors were identified. The annual growth rate of publications was 2.78%, with a surge after 1980, concurrent with the growth of specialized journals. International coauthorship, absent before 1970, increased exponentially with the formation of influential spine study groups. Reference publication year spectroscopy allowed us to identify 200 articles that comprise the historical roots of modern spine surgery and each of its subdisciplines. We mapped the emergence of new topics and saw a recent lexical evolution toward outcomes- and patient-centric terms. Female and minority coauthorship has increased since 1990, but remains low, and disparities across major publications persist.

CONCLUSION

The field of spine surgery was borne from pioneering individuals who published their findings in a variety of journals. The renaissance of spine surgery has been powered by international collaboration and is increasingly outcomes focused. While spine surgery is gradually becoming more diverse, there is a clear need for further promotion and outreach to under-represented populations.