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

Since 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 sector...

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Autores principales: El-Rashidy, Nora, Abdelrazik, Samir, Abuhmed, Tamer, Amer, Eslam, Ali, Farman, Hu, Jong-Wan, El-Sappagh, Shaker
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8303306/
https://www.ncbi.nlm.nih.gov/pubmed/34202587
http://dx.doi.org/10.3390/diagnostics11071155
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author El-Rashidy, Nora
Abdelrazik, Samir
Abuhmed, Tamer
Amer, Eslam
Ali, Farman
Hu, Jong-Wan
El-Sappagh, Shaker
author_facet El-Rashidy, Nora
Abdelrazik, Samir
Abuhmed, Tamer
Amer, Eslam
Ali, Farman
Hu, Jong-Wan
El-Sappagh, Shaker
author_sort El-Rashidy, Nora
collection PubMed
description Since 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-19.
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spelling pubmed-83033062021-07-25 Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic El-Rashidy, Nora Abdelrazik, Samir Abuhmed, Tamer Amer, Eslam Ali, Farman Hu, Jong-Wan El-Sappagh, Shaker Diagnostics (Basel) Review Since 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-19. MDPI 2021-06-24 /pmc/articles/PMC8303306/ /pubmed/34202587 http://dx.doi.org/10.3390/diagnostics11071155 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Review
El-Rashidy, Nora
Abdelrazik, Samir
Abuhmed, Tamer
Amer, Eslam
Ali, Farman
Hu, Jong-Wan
El-Sappagh, Shaker
Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic
title Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic
title_full Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic
title_fullStr Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic
title_full_unstemmed Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic
title_short Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic
title_sort comprehensive survey of using machine learning in the covid-19 pandemic
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8303306/
https://www.ncbi.nlm.nih.gov/pubmed/34202587
http://dx.doi.org/10.3390/diagnostics11071155
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