Keywords
Topic modeling
Machine learning
Topics
Journals
Trends
How to Cite
Abstract
Introduction: Topic detection can be used to identify trends in literature, providing valuable insight into the direction of the field. We developed a natural language processing (NLP) based method to identify topics from given abstracts and assessed the main topics of published articles by top medical journals in the last three years.
Methods: This study utilized a two-part methodology to extract and classify original articles published by four non-specialized medical journals; Lancet, New England Journal of Medicine, Journal of the American Medical Association, and British Medical Journal. The first part employed bibliometric data collection to search for original articles published between 2020 and 2022. The second part used an NLP approach based on the BERTopic model to classify the articles included into separate topics.
Results: The model was able to classify 1,540 articles out of the included 2,081 (79.42%) into 39 different topics in 11 fields. COVID-19-related and cancer treatment-related articles constituted approximately 25% and 7% of all published papers during 2020-2022 respectively. The study found that each of the included general medical journal tended to focus on certain topics more than others.
Conclusion: We identified a new methodology that can identify topics discussed in medical literature from abstracts as an input. We also demonstrated the potential of this methodology for analyzing trends in medical literature more efficiently and effectively. This study's methodology can be replicated on a larger scale with more papers, more journals, and over a longer period, highlighting the importance of further research using NLP models.
References
: Abdalla SM, Solomon H, Trinquart L, et al. What is considered as global health scholarship? A meta-knowledge analysis of global health journals and definitions
BMJ Global Health 2020;5:e002884
: Scaccia JP, Scott VC. 5335 days of Implementation Science: using natural language processing to examine publication trends and topics. Implement Sci. 2021;16(1):47. Published 2021 Apr 26. doi:10.1186/s13012-021-01120-4
: Jung KY, Kim T, Jung J, et al. The Effectiveness of Near-Field Communication Integrated with a Mobile Electronic Medical Record System: Emergency Department Simulation Study. JMIR Mhealth Uhealth. 2018;6(9):e11187. Published 2018 Sep 21. doi:10.2196/11187
: Lee M, Wang W, Yu H. Exploring supervised and unsupervised methods to detect topics in biomedical text. BMC Bioinformatics. 2006;7:140. Published 2006 Mar 16. doi:10.1186/1471-2105-7-140
: Vaswani A, Shazeer N, Parmar N, et al. Attention is All you Need. Advances in Neural Information Processing Systems. 2017;30:5998-6008. https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
: Grootendorst M. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv:220305794 [cs]. Published online March 11, 2022. https://arxiv.org/abs/2203.05794
: Al-Habsi J, Al-Hatmi A, Al-Saadi T. The Top 100 Most Cited Neurosurgical Articles on COVID-19: A Bibliometric Analysis. World Neurosurg. 2023;170:22-27.e21. doi:10.1016/j.wneu.2022.11.133
: Berrang-Ford L, Sietsma AJ, Callaghan M, et al. Systematic mapping of global research on climate and health: a machine learning review. Lancet Planet Health. 2021;5(8):e514-e525. doi:10.1016/S2542-5196(21)00179-0
: Song Y, Ni Z, Li Y, et al. Exploring the landscape, hot topics, and trends of bariatric metabolic surgery with machine learning and bibliometric analysis. Ther Adv Gastrointest Endosc. 2022;15:26317745221111944. Published 2022 Jul 28. doi:10.1177/26317745221111944
: Sing DC, Metz LN, Dudli S. Machine Learning-Based Classification of 38 Years of Spine-Related Literature Into 100 Research Topics. Spine (Phila Pa 1976). 2017;42(11):863-870. doi:10.1097/BRS.0000000000002079
: Danilov GV, Shifrin MA, Kotik KV, et al. Artificial Intelligence in Neurosurgery: a Systematic Review Using Topic Modeling. Part I: Major Research Areas. Sovrem Tekhnologii Med. 2021;12(5):106-112. doi:10.17691/stm2020.12.5.12.
: Ng QX, Yau CE, Lim YL, Wong LKT, Liew TM. Public sentiment on the global outbreak of monkeypox: an unsupervised machine learning analysis of 352,182 twitter posts. Public Health. 2022;213:1-4. doi:10.1016/j.puhe.2022.09.008
: Baird A, Xia Y, Cheng Y. Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis. JAMIA Open. 2022;5(2):ooac028. Published 2022 Apr 27. doi:10.1093/jamiaopen/ooac028
: Zankadi H, Idrissi A, Daoudi N, Hilal I. Identifying learners' topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques [published online ahead of print, 2022 Nov 4]. Educ Inf Technol (Dordr). 2022;1-18. doi:10.1007/s10639-022-11373-1
: Dieng AB, Ruiz FJR, Blei DM. Topic Modeling in Embedding Spaces. Transactions of the Association for Computational Linguistics. 2020;8:439-453. doi:https://doi.org/10.1162/tacl_a_00325
: Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv.org. Published October 11, 2018. https://arxiv.org/abs/1810.04805
: AlRyalat SAS, Malkawi LW, Momani SM. Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases. J Vis Exp. 2019;(152):10.3791/58494. Published 2019 Oct 24. doi:10.3791/58494
: Palayew A, Norgaard O, Safreed-Harmon K, Andersen TH, Rasmussen LN, Lazarus JV. Pandemic publishing poses a new COVID-19 challenge. Nat Hum Behav. 2020;4(7):666-669. doi:10.1038/s41562-020-0911-0
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