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A Comprehensive Review of Machine Learning Used to Combat COVID-19

Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and de...

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Autores principales: Gomes, Rahul, Kamrowski, Connor, Langlois, Jordan, Rozario, Papia, Dircks, Ian, Grottodden, Keegan, Martinez, Matthew, Tee, Wei Zhong, Sargeant, Kyle, LaFleur, Corbin, Haley, Mitchell
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406981/
https://www.ncbi.nlm.nih.gov/pubmed/36010204
http://dx.doi.org/10.3390/diagnostics12081853
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author Gomes, Rahul
Kamrowski, Connor
Langlois, Jordan
Rozario, Papia
Dircks, Ian
Grottodden, Keegan
Martinez, Matthew
Tee, Wei Zhong
Sargeant, Kyle
LaFleur, Corbin
Haley, Mitchell
author_facet Gomes, Rahul
Kamrowski, Connor
Langlois, Jordan
Rozario, Papia
Dircks, Ian
Grottodden, Keegan
Martinez, Matthew
Tee, Wei Zhong
Sargeant, Kyle
LaFleur, Corbin
Haley, Mitchell
author_sort Gomes, Rahul
collection PubMed
description Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.
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spelling pubmed-94069812022-08-26 A Comprehensive Review of Machine Learning Used to Combat COVID-19 Gomes, Rahul Kamrowski, Connor Langlois, Jordan Rozario, Papia Dircks, Ian Grottodden, Keegan Martinez, Matthew Tee, Wei Zhong Sargeant, Kyle LaFleur, Corbin Haley, Mitchell Diagnostics (Basel) Review Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19. MDPI 2022-07-31 /pmc/articles/PMC9406981/ /pubmed/36010204 http://dx.doi.org/10.3390/diagnostics12081853 Text en © 2022 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
Gomes, Rahul
Kamrowski, Connor
Langlois, Jordan
Rozario, Papia
Dircks, Ian
Grottodden, Keegan
Martinez, Matthew
Tee, Wei Zhong
Sargeant, Kyle
LaFleur, Corbin
Haley, Mitchell
A Comprehensive Review of Machine Learning Used to Combat COVID-19
title A Comprehensive Review of Machine Learning Used to Combat COVID-19
title_full A Comprehensive Review of Machine Learning Used to Combat COVID-19
title_fullStr A Comprehensive Review of Machine Learning Used to Combat COVID-19
title_full_unstemmed A Comprehensive Review of Machine Learning Used to Combat COVID-19
title_short A Comprehensive Review of Machine Learning Used to Combat COVID-19
title_sort comprehensive review of machine learning used to combat covid-19
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406981/
https://www.ncbi.nlm.nih.gov/pubmed/36010204
http://dx.doi.org/10.3390/diagnostics12081853
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