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A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics

The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefo...

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Autores principales: Zheng, Yichao, Zhu, Yinheng, Ji, Mengqi, Wang, Rongpin, Liu, Xinfeng, Zhang, Mudan, Liu, Jun, Zhang, Xiaochun, Qin, Choo Hui, Fang, Lu, Ma, Shaohua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7396968/
https://www.ncbi.nlm.nih.gov/pubmed/32838344
http://dx.doi.org/10.1016/j.patter.2020.100092
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author Zheng, Yichao
Zhu, Yinheng
Ji, Mengqi
Wang, Rongpin
Liu, Xinfeng
Zhang, Mudan
Liu, Jun
Zhang, Xiaochun
Qin, Choo Hui
Fang, Lu
Ma, Shaohua
author_facet Zheng, Yichao
Zhu, Yinheng
Ji, Mengqi
Wang, Rongpin
Liu, Xinfeng
Zhang, Mudan
Liu, Jun
Zhang, Xiaochun
Qin, Choo Hui
Fang, Lu
Ma, Shaohua
author_sort Zheng, Yichao
collection PubMed
description The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.
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spelling pubmed-73969682020-08-03 A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics Zheng, Yichao Zhu, Yinheng Ji, Mengqi Wang, Rongpin Liu, Xinfeng Zhang, Mudan Liu, Jun Zhang, Xiaochun Qin, Choo Hui Fang, Lu Ma, Shaohua Patterns (N Y) Article The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics. Elsevier 2020-08-03 /pmc/articles/PMC7396968/ /pubmed/32838344 http://dx.doi.org/10.1016/j.patter.2020.100092 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Zheng, Yichao
Zhu, Yinheng
Ji, Mengqi
Wang, Rongpin
Liu, Xinfeng
Zhang, Mudan
Liu, Jun
Zhang, Xiaochun
Qin, Choo Hui
Fang, Lu
Ma, Shaohua
A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics
title A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics
title_full A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics
title_fullStr A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics
title_full_unstemmed A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics
title_short A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics
title_sort learning-based model to evaluate hospitalization priority in covid-19 pandemics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7396968/
https://www.ncbi.nlm.nih.gov/pubmed/32838344
http://dx.doi.org/10.1016/j.patter.2020.100092
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