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Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance

COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine...

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Autores principales: Shahid, Osama, Nasajpour, Mohammad, Pouriyeh, Seyedamin, Parizi, Reza M., Han, Meng, Valero, Maria, Li, Fangyu, Aledhari, Mohammed, Sheng, Quan Z.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7987503/
https://www.ncbi.nlm.nih.gov/pubmed/33771732
http://dx.doi.org/10.1016/j.jbi.2021.103751
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author Shahid, Osama
Nasajpour, Mohammad
Pouriyeh, Seyedamin
Parizi, Reza M.
Han, Meng
Valero, Maria
Li, Fangyu
Aledhari, Mohammed
Sheng, Quan Z.
author_facet Shahid, Osama
Nasajpour, Mohammad
Pouriyeh, Seyedamin
Parizi, Reza M.
Han, Meng
Valero, Maria
Li, Fangyu
Aledhari, Mohammed
Sheng, Quan Z.
author_sort Shahid, Osama
collection PubMed
description COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.
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spelling pubmed-79875032021-03-24 Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance Shahid, Osama Nasajpour, Mohammad Pouriyeh, Seyedamin Parizi, Reza M. Han, Meng Valero, Maria Li, Fangyu Aledhari, Mohammed Sheng, Quan Z. J Biomed Inform Original Research COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus. Elsevier Inc. 2021-05 2021-03-24 /pmc/articles/PMC7987503/ /pubmed/33771732 http://dx.doi.org/10.1016/j.jbi.2021.103751 Text en © 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Original Research
Shahid, Osama
Nasajpour, Mohammad
Pouriyeh, Seyedamin
Parizi, Reza M.
Han, Meng
Valero, Maria
Li, Fangyu
Aledhari, Mohammed
Sheng, Quan Z.
Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance
title Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance
title_full Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance
title_fullStr Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance
title_full_unstemmed Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance
title_short Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance
title_sort machine learning research towards combating covid-19: virus detection, spread prevention, and medical assistance
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7987503/
https://www.ncbi.nlm.nih.gov/pubmed/33771732
http://dx.doi.org/10.1016/j.jbi.2021.103751
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