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

Arzu Baygül Eden1, Alev Bakir Kayi2, Mustafa Genco Erdem3

  • 1Koç University, School of Medicine, Department of Biostatistics, Istanbul, Turkey.

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Summary

This bibliometric analysis reveals key trends in machine learning (ML) and artificial intelligence (AI) research for coronavirus disease 2019 (COVID-19). It highlights leading authors, institutions, and countries driving advancements in ML applications for COVID-19.

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Area of Science:

  • * Focuses on the intersection of computer science, engineering, and science & technology.
  • * Analyzes the burgeoning field of machine learning (ML) and artificial intelligence (AI) in biomedical research.

Background:

  • * Machine learning (ML) and artificial intelligence (AI) are increasingly vital for coronavirus disease 2019 (COVID-19) research.
  • * These technologies support diagnosis, prognosis, therapy, and public health strategies.
  • * Bibliometric analysis is employed to assess the quality and impact of scholarly work in this domain.

Purpose of the Study:

  • * To conduct a bibliometric analysis of machine learning (ML) applications in coronavirus disease 2019 (COVID-19) research.
  • * To identify influential authors, institutions, countries, and publications in this research area.
  • * To provide insights for researchers and clinicians utilizing ML for COVID-19 studies.

Main Methods:

  • * A comprehensive literature search was performed using Web of Science (WoS).
  • * Keywords included "machine learning," "artificial intelligence," and "COVID-19."
  • * Network visualization was analyzed using VOSviewer 1.6.19 by two independent reviewers.

Main Results:

  • * The United States, China, and India are the leading countries in ML-based COVID-19 research output and citations.
  • * Tao Huang, Fadi Al-Turjman, and Imran Ashraf are identified as highly prolific authors.
  • * Key research areas include computer science and engineering, with significant contributions from institutions like Harvard Medical School and Huazhong University of Science and Technology.

Conclusions:

  • * Bibliometric data offers valuable insights into productive researchers, countries, and high-impact publications in ML for COVID-19.
  • * Advancements in ML and AI modeling are anticipated due to new data and methodologies emerging from the pandemic.
  • * This research highlights the pioneering role of ML and AI in addressing COVID-19.