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Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review

The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data...

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Detalles Bibliográficos
Autores principales: Bansal, Agam, Padappayil, Rana Prathap, Garg, Chandan, Singal, Anjali, Gupta, Mohak, Klein, Allan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395799/
https://www.ncbi.nlm.nih.gov/pubmed/32740678
http://dx.doi.org/10.1007/s10916-020-01617-3
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author Bansal, Agam
Padappayil, Rana Prathap
Garg, Chandan
Singal, Anjali
Gupta, Mohak
Klein, Allan
author_facet Bansal, Agam
Padappayil, Rana Prathap
Garg, Chandan
Singal, Anjali
Gupta, Mohak
Klein, Allan
author_sort Bansal, Agam
collection PubMed
description The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.
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spelling pubmed-73957992020-08-03 Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review Bansal, Agam Padappayil, Rana Prathap Garg, Chandan Singal, Anjali Gupta, Mohak Klein, Allan J Med Syst Mobile & Wireless Health The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls. Springer US 2020-08-01 2020 /pmc/articles/PMC7395799/ /pubmed/32740678 http://dx.doi.org/10.1007/s10916-020-01617-3 Text en © Springer Science+Business Media, LLC, part of Springer Nature 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Mobile & Wireless Health
Bansal, Agam
Padappayil, Rana Prathap
Garg, Chandan
Singal, Anjali
Gupta, Mohak
Klein, Allan
Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review
title Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review
title_full Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review
title_fullStr Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review
title_full_unstemmed Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review
title_short Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review
title_sort utility of artificial intelligence amidst the covid 19 pandemic: a review
topic Mobile & Wireless Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395799/
https://www.ncbi.nlm.nih.gov/pubmed/32740678
http://dx.doi.org/10.1007/s10916-020-01617-3
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