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

ABSTRACT: 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 pro

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC7396968 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature