Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

dc.contributor.affiliationUniversidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Informacióngl
dc.contributor.authorEl Rashidy, Nora
dc.contributor.authorAbdelrazek, Samir
dc.contributor.authorAbuhmed, Tamer
dc.contributor.authorAmer, Eslam
dc.contributor.authorAli, Farman
dc.contributor.authorHu, Jon Wan
dc.contributor.authorEl-Sappagh, Shaker
dc.date.accessioned2021-07-30T11:18:33Z
dc.date.available2021-07-30T11:18:33Z
dc.date.issued2021
dc.description.abstractSince December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19gl
dc.description.peerreviewedSIgl
dc.description.sponsorshipThis work was supported by a 2021 Incheon National University Research Grant. This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A4A4079299)gl
dc.identifier.citationDiagnostics 2021, 11(7), 1155; https://doi.org/10.3390/diagnostics11071155gl
dc.identifier.doi10.3390/diagnostics11071155
dc.identifier.essn2075-4418
dc.identifier.urihttp://hdl.handle.net/10347/26659
dc.language.isoenggl
dc.publisherMDPIgl
dc.relation.publisherversionhttps://doi.org/10.3390/diagnostics11071155gl
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)gl
dc.rightsAtribución 4.0 Internacional
dc.rights.accessRightsopen accessgl
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial intelligencegl
dc.subjectDeep learninggl
dc.subjectCOVID_19gl
dc.titleComprehensive Survey of Using Machine Learning in the COVID-19 Pandemicgl
dc.typejournal articlegl
dc.type.hasVersionVoRgl
dspace.entity.typePublication

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