Artificial intelligence-based approaches for COVID-19 patient management

During the highly infectious pandemic of coronavirus disease 2019 (COVID-19), artificial intelligence (AI) has provided support in addressing challenges and accelerating achievements in controlling this public health crisis. It has been applied in fields varying from outbreak forecasting to patient...

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Detalles Bibliográficos
Autores principales: Lan, Lan, Sun, Wenbo, Xu, Dan, Yu, Minhua, Xiao, Feng, Hu, Huijuan, Xu, Haibo, Wang, Xinghuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier B.V. on behalf of Chinese Medical Association. 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189732/
https://www.ncbi.nlm.nih.gov/pubmed/34447600
http://dx.doi.org/10.1016/j.imed.2021.05.005
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author Lan, Lan
Sun, Wenbo
Xu, Dan
Yu, Minhua
Xiao, Feng
Hu, Huijuan
Xu, Haibo
Wang, Xinghuan
author_facet Lan, Lan
Sun, Wenbo
Xu, Dan
Yu, Minhua
Xiao, Feng
Hu, Huijuan
Xu, Haibo
Wang, Xinghuan
author_sort Lan, Lan
collection PubMed
description During the highly infectious pandemic of coronavirus disease 2019 (COVID-19), artificial intelligence (AI) has provided support in addressing challenges and accelerating achievements in controlling this public health crisis. It has been applied in fields varying from outbreak forecasting to patient management and drug/vaccine development. In this paper, we specifically review the current status of AI-based approaches for patient management. Limitations and challenges still exist, and further needs are highlighted.
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spelling pubmed-81897322021-06-10 Artificial intelligence-based approaches for COVID-19 patient management Lan, Lan Sun, Wenbo Xu, Dan Yu, Minhua Xiao, Feng Hu, Huijuan Xu, Haibo Wang, Xinghuan Intell Med Article During the highly infectious pandemic of coronavirus disease 2019 (COVID-19), artificial intelligence (AI) has provided support in addressing challenges and accelerating achievements in controlling this public health crisis. It has been applied in fields varying from outbreak forecasting to patient management and drug/vaccine development. In this paper, we specifically review the current status of AI-based approaches for patient management. Limitations and challenges still exist, and further needs are highlighted. The Authors. Published by Elsevier B.V. on behalf of Chinese Medical Association. 2021-05 2021-06-10 /pmc/articles/PMC8189732/ /pubmed/34447600 http://dx.doi.org/10.1016/j.imed.2021.05.005 Text en © 2021 The Authors. Published by Elsevier B.V. on behalf of Chinese Medical Association. 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 Article
Lan, Lan
Sun, Wenbo
Xu, Dan
Yu, Minhua
Xiao, Feng
Hu, Huijuan
Xu, Haibo
Wang, Xinghuan
Artificial intelligence-based approaches for COVID-19 patient management
title Artificial intelligence-based approaches for COVID-19 patient management
title_full Artificial intelligence-based approaches for COVID-19 patient management
title_fullStr Artificial intelligence-based approaches for COVID-19 patient management
title_full_unstemmed Artificial intelligence-based approaches for COVID-19 patient management
title_short Artificial intelligence-based approaches for COVID-19 patient management
title_sort artificial intelligence-based approaches for covid-19 patient management
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189732/
https://www.ncbi.nlm.nih.gov/pubmed/34447600
http://dx.doi.org/10.1016/j.imed.2021.05.005
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