COVID-19 studies involving machine learning methods: A bibliometric study

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dc.contributor.authorEden, Arzu Baygul
dc.contributor.authorKayi, Alev Bakir
dc.contributor.authorErdem, Mustafa Genco
dc.contributor.authorDemirci, Mehmet
dc.date.accessioned2024-03-13T10:35:33Z
dc.date.available2024-03-13T10:35:33Z
dc.date.issued2023
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractBackground:Machine learning (ML) and artificial intelligence (AI) techniques are gaining popularity as effective tools for coronavirus disease of 2019 (COVID-19) research. These strategies can be used in diagnosis, prognosis, therapy, and public health management. Bibliometric analysis quantifies the quality and impact of scholarly publications. ML in COVID-19 research is the focus of this bibliometric analysis.Methods:A comprehensive literature study found ML-based COVID-19 research. Web of Science (WoS) was used for the study. The searches included machine learning, artificial intelligence, and COVID-19. To find all relevant studies, 2 reviewers searched independently. The network visualization was analyzed using VOSviewer 1.6.19.Results:In the WoS Core, the average citation count was 13.6 +/- 41.3. The main research areas were computer science, engineering, and science and technology. According to document count, Tao Huang wrote 14 studies, Fadi Al-Turjman wrote 11, and Imran Ashraf wrote 11. The US, China, and India produced the most studies and citations. The most prolific research institutions were Harvard Medical School, Huazhong University of Science and Technology, and King Abdulaziz University. In contrast, Nankai University, Oxford, and Imperial College London were the most mentioned organizations, reflecting their significant research contributions. First, Covid-19 appeared 1983 times, followed by machine learning and deep learning. The US Department of Health and Human Services funded this topic most heavily. Huang Tao, Feng Kaiyan, and Ashraf Imran pioneered bibliographic coupling.Conclusion:This study provides useful insights for academics and clinicians studying COVID-19 using ML. Through bibliometric data analysis, scholars can learn about highly recognized and productive authors and countries, as well as the publications with the most citations and keywords. New data and methodologies from the pandemic are expected to advance ML and AI modeling. It is crucial to recognize that these studies will pioneer this subject.en_US
dc.identifier.doi10.1097/MD.0000000000035564
dc.identifier.issn0025-7974
dc.identifier.issn1536-5964
dc.identifier.issue43en_US
dc.identifier.pmid37904407en_US
dc.identifier.scopus2-s2.0-85175592591en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1097/MD.0000000000035564
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4485
dc.identifier.volume102en_US
dc.identifier.wosWOS:001124112800103en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherLippincott Williams & Wilkinsen_US
dc.relation.ispartofMedicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectartificial intelligenceen_US
dc.subjectbibliometric analysisen_US
dc.subjectCOVID-19en_US
dc.subjectmachine learningen_US
dc.titleCOVID-19 studies involving machine learning methods: A bibliometric studyen_US
dc.typeReview Articleen_US

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WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu