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

Summary The emergence of novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on the healthcare systems. Although the majority of infected patients have non-severe symptoms and can be managed at home, some individuals may develop severe disease and are demanding the hospital admission. Therefore, it becomes paramount to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a 4-variable assessment model, including lymphocyte, lactate dehydrogenase (LDH), C-reactive protein (CRP) and neutrophil, is established and validated using the XGBoost algorithm. This model is found effective to identify 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 the healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.