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Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

Author

Listed:
  • Ignacio Rodríguez-Rodríguez

    (Protein Structure and Bioinformatics Resech Group, Department of Experimental Medical Science, Lund University, SE-221 84 Lund, Sweden)

  • José-Víctor Rodríguez

    (Departamento de Tecnologías de la Información y las Comunicaciones, School of Telecommunications Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain)

  • Niloofar Shirvanizadeh

    (Protein Structure and Bioinformatics Resech Group, Department of Experimental Medical Science, Lund University, SE-221 84 Lund, Sweden)

  • Andrés Ortiz

    (Departamento de Ingeniería de Comunicaciones, School of Telecommunications Engineering, Universidad de Málaga, 29071 Málaga, Spain)

  • Domingo-Javier Pardo-Quiles

    (Departamento de Tecnologías de la Información y las Comunicaciones, School of Telecommunications Engineering, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain)

Abstract

The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.

Suggested Citation

  • Ignacio Rodríguez-Rodríguez & José-Víctor Rodríguez & Niloofar Shirvanizadeh & Andrés Ortiz & Domingo-Javier Pardo-Quiles, 2021. "Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining," IJERPH, MDPI, vol. 18(16), pages 1-29, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8578-:d:614174
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    as
    1. Loet Leydesdorff & Lutz Bornmann, 2012. "Mapping (USPTO) patent data using overlays to Google Maps," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(7), pages 1442-1458, July.
    2. Jiban K. Pal, 2021. "Visualizing the knowledge outburst in global research on COVID-19," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4173-4193, May.
    3. Ludo Waltman & Nees Jan van Eck, 2012. "A new methodology for constructing a publication‐level classification system of science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    4. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    5. Guillermo Gallacher & Iqbal Hossain, 2020. "Remote Work and Employment Dynamics under COVID-19: Evidence from Canada," Canadian Public Policy, University of Toronto Press, vol. 46(S1), pages 44-54, July.
    6. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    7. Kai Li & Jason Rollins & Erjia Yan, 2018. "Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 1-20, April.
    8. Ludo Waltman & Nees Jan Eck, 2012. "A new methodology for constructing a publication-level classification system of science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    9. Israel Edem Agbehadji & Bankole Osita Awuzie & Alfred Beati Ngowi & Richard C. Millham, 2020. "Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing," IJERPH, MDPI, vol. 17(15), pages 1-16, July.
    10. Janny C. Hoekstra & Peter S. H. Leeflang, 2020. "Marketing in the era of COVID-19," Italian Journal of Marketing, Springer, vol. 2020(4), pages 249-260, December.
    11. Anne-Wil Harzing & Satu Alakangas, 2016. "Google Scholar, Scopus and the Web of Science: a longitudinal and cross-disciplinary comparison," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 787-804, February.
    12. Halevi, Gali & Moed, Henk & Bar-Ilan, Judit, 2017. "Suitability of Google Scholar as a source of scientific information and as a source of data for scientific evaluation—Review of the Literature," Journal of Informetrics, Elsevier, vol. 11(3), pages 823-834.
    13. Nees Jan Eck & Ludo Waltman & Ed C. M. Noyons & Reindert K. Buter, 2010. "Automatic term identification for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(3), pages 581-596, March.
    14. M. M. Kessler, 1963. "Bibliographic coupling between scientific papers," American Documentation, Wiley Blackwell, vol. 14(1), pages 10-25, January.
    15. Milad Haghani & Pegah Varamini, 2021. "Temporal evolution, most influential studies and sleeping beauties of the coronavirus literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7005-7050, August.
    16. Philippe Mongeon & Adèle Paul-Hus, 2016. "The journal coverage of Web of Science and Scopus: a comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 213-228, January.
    17. Antonio Fusco & Grazia Dicuonzo & Vittorio Dell’Atti & Marco Tatullo, 2020. "Blockchain in Healthcare: Insights on COVID-19," IJERPH, MDPI, vol. 17(19), pages 1-12, September.
    18. Peter Dannenberg & Martina Fuchs & Tim Riedler & Cathrin Wiedemann, 2020. "Digital Transition by COVID‐19 Pandemic? The German Food Online Retail," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 111(3), pages 543-560, July.
    19. Erjia Yan & Ying Ding & Elin K. Jacob, 2012. "Overlaying communities and topics: an analysis on publication networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 499-513, February.
    20. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    21. Valderrama-Zurián, Juan-Carlos & Aguilar-Moya, Remedios & Melero-Fuentes, David & Aleixandre-Benavent, Rafael, 2015. "A systematic analysis of duplicate records in Scopus," Journal of Informetrics, Elsevier, vol. 9(3), pages 570-576.
    22. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    23. Junwen Zhu & Weishu Liu, 2020. "A tale of two databases: the use of Web of Science and Scopus in academic papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 321-335, April.
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