Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis.

Background/Aim: To evaluate the research trends in coronavirus disease (COVID-19). Materials and Methods: A bibliometric analysis was performed using a machine learning bibliometric methodology. Information regarding publication outputs, countries, institutions, journals, keywords, funding and citation counts was retrieved from Scopus database. Results: A total of 1883 eligible papers were returned. An exponential increase in the COVID-19 publications occurred in the last months. As expected, China produced the majority of articles, followed by the United States of America, the United Kingdom and Italy. There is greater collaboration between highly contributing authors and institutions. The "BMJ" published the highest number of papers (n=129) and "The Lancet" had the most citations (n=1439). The most ubiquitous topic was COVID-19 clinical features. Conclusion: This bibliometric analysis presents the most influential references related to COVID-19 during this time and could be useful to improve understanding and management of COVID-19. [ABSTRACT FROM AUTHOR]