Two Decades of Rheumatology Research (2000-2023): A Dynamic Topic Modeling Perspective

Background: Rheumatology has experience notably changes in last decades. New drugs, including biologic agents and janus kinase inhibitors, have bloosom. Concepts such as window of opportunity, arthralgia suspicious for progression, or difficult-to-treat rheumatoid arthritis have appeared; and new management approaches and strategies such as treat-to-target have become popular. Statistical learning methods, gene therapy, telemedicine or precision medicine are other advancements that have gained relevance in the field. To better characterise the research landscape and advances in rheumatology, automatic and efficient approaches based on natural language processing should be used. The objective of this study is to use topic modeling techniques to uncover key topics and trends in the rheumatology research conducted in the last 23 years. Methods: This study analysed 96,004 abstracts published between 2000 and December 31, 2023, drawn from 34 specialised rheumatology journals obtained from PubMed. BERTopic, a novel topic modeling approach that considers semantic relationships among words and their context, was used to uncover topics. Up to 30 different models were trained. Based on the number of topics, outliers and topic coherence score, two of them were finally selected, and the topics manually labeled by two rheumatologists. Word clouds and hierarchical clustering visualizations were computed. Finally, hot and cold trends were identified using linear regression models. Results: Abstracts were classified into 45 and 47 topics. The most frequent topics were rheumatoid arthritis, systemic lupus erythematosus and osteoarthritis. Expected topics such as COVID-19 or JAK inhibitors were identified after conducting the dynamic topic modeling. Topics such as spinal surgery or bone fractures have gained relevance in last years, however, antiphospholipid syndrome, or septic arthritis have lost momentum. Conclusions: Our study utilized advanced natural language processing techniques to analyse the rheumatology research landscape, and identify key themes and emerging trends. The results highlight the dynamic and varied nature of rheumatology research, illustrating how interest in certain topics have shifted over time. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data used in this manuscript is available online at https://pubmed.ncbi.nlm.nih.gov/.