{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T22:30:48Z","timestamp":1780093848941,"version":"3.54.0"},"reference-count":20,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T00:00:00Z","timestamp":1709683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T00:00:00Z","timestamp":1709683200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Neurotrauma Reports"],"published-print":{"date-parts":[[2024,4,1]]},"abstract":"<jats:p>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 \u201chot\u201d and \u201ccold\u201d 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: \u201cRehabilitation,\u201d \u201cMolecular Mechanisms of TBI,\u201d \u201cConcussion,\u201d \u201cRepetitive Head Impacts,\u201d \u201cSurgical Interventions,\u201d \u201cBiomarkers,\u201d \u201cIntracranial Pressure,\u201d \u201cPosttraumatic Neurodegeneration,\u201d \u201cChronic Traumatic Encephalopathy,\u201d and \u201cBlast Induced TBI,\u201d while our trend analysis indicated that the hottest topics of the current decade were \u201cGenomics,\u201d \u201cSex Hormones,\u201d and \u201cDiffusion Tensor Imaging,\u201d while the cooling topics were \u201cPosttraumatic Sleep,\u201d \u201cSensory Functions,\u201d and \u201cHyperosmolar Therapies.\u201d 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.<\/jats:p>","DOI":"10.1089\/neur.2023.0102","type":"journal-article","created":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:33:18Z","timestamp":1709811198000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Exploiting Natural Language Processing to Unveil Topics and Trends of Traumatic Brain Injury Research"],"prefix":"10.1177","volume":"5","author":[{"given":"Mert","family":"Karabacak","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankita","family":"Jain","sequence":"additional","affiliation":[{"name":"School of Medicine, New York Medical College, Valhalla, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pemla","family":"Jagtiani","sequence":"additional","affiliation":[{"name":"School of Medicine, SUNY Downstate Health Sciences University, New York, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zachary L.","family":"Hickman","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA."},{"name":"Department of Neurosurgery, NYC Health + Hospitals\/Elmhurst, New York, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristen","family":"Dams-O'Connor","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, New York, USA."},{"name":"Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantinos","family":"Margetis","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,3,6]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2013.22"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.3171\/2017.10.JNS17352"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/jrsm.1123"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2016-012545"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.282.7.634"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12917-021-03000-x"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.1177\/20552076221133696"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2023.03.001"},{"key":"e_1_3_6_10_2","unstructured":"Grootendorst M. BERTopic: Neural Topic Modeling with a Class-Based TF-IDF Procedure. 2022. Available from: https:\/\/arxiv.org\/abs\/2203.05794v1 [Last accessed: May13 2023]."},{"key":"e_1_3_6_11_2","first-page":"4171","article-title":"BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding","author":"Devlin J","year":"2019","unstructured":"Devlin J, , Chang MW, , Lee K, et al. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of the 2019 Conference of the North Association for Computational Linguistics: Minneapolis, Minnesota;, 2019; pp. 4171\u20134186; doi: 10.18653\/v1\/N19-1423","journal-title":"In: Proceedings of the 2019 Conference of the North Association for Computational Linguistics: Minneapolis, Minnesota;"},{"key":"e_1_3_6_12_2","doi-asserted-by":"publisher","DOI":"10.26421\/JDI3.4-5"},{"key":"e_1_3_6_13_2","unstructured":"Bird S Klein E Loper E. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O'Reilly Media Inc.: Sebastopol CA; 2009."},{"issue":"1","key":"e_1_3_6_14_2","first-page":"3","article-title":"How to identify hot topics in psychology using topic modeling","volume":"226","author":"Bittermann A","year":"2018","unstructured":"Bittermann A, , Fischer A. How to identify hot topics in psychology using topic modeling. Zeitschr Psychol, 2018; 226(1):3\u201313; doi: 10.1027\/2151-2604\/a000318","journal-title":"Zeitschr Psychol"},{"key":"e_1_3_6_15_2","doi-asserted-by":"publisher","DOI":"10.1097\/BRS.0000000000002079"},{"key":"e_1_3_6_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.wnsx.2023.100182"},{"key":"e_1_3_6_17_2","doi-asserted-by":"publisher","DOI":"10.3389\/fsurg.2022.1037978"},{"key":"e_1_3_6_18_2","first-page":"993","article-title":"Latent Dirichlet Allocation","volume":"3","author":"Blei DM","year":"2003","unstructured":"Blei DM, , Ng AY, , Jordan MI. Latent Dirichlet Allocation. J Mach Learn Res, 2003; 3(null):993\u20131022.","journal-title":"J Mach Learn Res"},{"key":"e_1_3_6_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2022.103933"},{"key":"e_1_3_6_20_2","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2019.00261"},{"key":"e_1_3_6_21_2","doi-asserted-by":"publisher","DOI":"10.1097\/RMR.0000000000000062"}],"container-title":["Neurotrauma Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1089\/neur.2023.0102","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/neur.2023.0102","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/neur.2023.0102","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T22:48:53Z","timestamp":1777675733000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1089\/neur.2023.0102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,6]]},"references-count":20,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,4,1]]}},"alternative-id":["10.1089\/neur.2023.0102"],"URL":"https:\/\/doi.org\/10.1089\/neur.2023.0102","relation":{},"ISSN":["2689-288X","2689-288X"],"issn-type":[{"value":"2689-288X","type":"print"},{"value":"2689-288X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,6]]},"article-number":"neur.2023.0102"}}