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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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Detalles Bibliográficos
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
Descripción
Sumario: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.