Literature DB >> 32503819

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

Francesca DE Felice1, Antonella Polimeni2.   

Abstract

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. Copyright
© 2020, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Entities:  

Keywords:  COVID-19; bibliometric analysis; coronavirus; machine learning; management

Mesh:

Year:  2020        PMID: 32503819     DOI: 10.21873/invivo.11951

Source DB:  PubMed          Journal:  In Vivo        ISSN: 0258-851X            Impact factor:   2.155


  18 in total

1.  Contribution of Deep-Learning Techniques Toward Fighting COVID-19: A Bibliometric Analysis of Scholarly Production During 2020.

Authors:  Janneth Chicaiza; Stephany D Villota; Paola G Vinueza-Naranjo; Ruben Rumipamba-Zambrano
Journal:  IEEE Access       Date:  2022-03-11       Impact factor: 3.476

2.  Dyslexia: A Bibliometric and Visualization Analysis.

Authors:  Yanqi Wu; Yanxia Cheng; Xianlin Yang; Wenyan Yu; Yuehua Wan
Journal:  Front Public Health       Date:  2022-06-23

Review 3.  Applications of artificial intelligence in battling against covid-19: A literature review.

Authors:  Mohammad-H Tayarani N
Journal:  Chaos Solitons Fractals       Date:  2020-10-03       Impact factor: 5.944

4.  COVID-19 and ophthalmology: A scientometric analysis.

Authors:  Gagan Kalra; Rishemjit Kaur; Parul Ichhpujani; Rutvi Chahal; Suresh Kumar
Journal:  Indian J Ophthalmol       Date:  2021-05       Impact factor: 1.848

5.  ACE2 Is a Prognostic Biomarker and Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma: Implication for COVID-19.

Authors:  Xinhao Niu; Zhe Zhu; Enming Shao; Juan Bao
Journal:  J Oncol       Date:  2021-01-28       Impact factor: 4.375

Review 6.  How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future.

Authors:  Yashpal Singh Malik; Shubhankar Sircar; Sudipta Bhat; Mohd Ikram Ansari; Tripti Pande; Prashant Kumar; Basavaraj Mathapati; Ganesh Balasubramanian; Rahul Kaushik; Senthilkumar Natesan; Sayeh Ezzikouri; Mohamed E El Zowalaty; Kuldeep Dhama
Journal:  Rev Med Virol       Date:  2020-12-19       Impact factor: 11.043

7.  Most notable 100 articles of COVID-19: an Altmetric study based on bibliometric analysis.

Authors:  Betul Borku Uysal; Mehmet Sami Islamoglu; Suna Koc; Mehmet Karadag; Mehmet Dokur
Journal:  Ir J Med Sci       Date:  2021-01-18       Impact factor: 1.568

8.  A Systematic Review and Bibliometric Analysis of the Scientific Literature on the Early Phase of COVID-19 in Italy.

Authors:  Federica Turatto; Elena Mazzalai; Federica Pagano; Giuseppe Migliara; Paolo Villari; Corrado De Vito
Journal:  Front Public Health       Date:  2021-06-22

9.  Mapping the situation of research on coronavirus disease-19 (COVID-19): a preliminary bibliometric analysis during the early stage of the outbreak.

Authors:  Sa'ed H Zyoud; Samah W Al-Jabi
Journal:  BMC Infect Dis       Date:  2020-08-01       Impact factor: 3.090

10.  Bibliometric Analysis on COVID-19: A Comparison of Research Between English and Chinese Studies.

Authors:  Jingchun Fan; Ya Gao; Na Zhao; Runjing Dai; Hailiang Zhang; Xiaoyan Feng; Guoxiu Shi; Jinhui Tian; Che Chen; Brett D Hambly; Shisan Bao
Journal:  Front Public Health       Date:  2020-08-14
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