Summary
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.
【저자키워드】 DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem, 【초록키워드】 COVID-19, severe COVID-19, Hospitalization, neutrophil, Epidemics, C-reactive protein, Symptom, progression, novel coronavirus disease, lactate dehydrogenase, lymphocyte, sensitivity, specificity, Deterioration, Accuracy, severity of COVID-19, healthcare, Algorithm, Pandemics, Hospital admission, resource, Admission, Critical, predict, Healthcare systems, Precision, individual, measure, effective, identify, develop, the disease, majority, infected patient, severe symptom, 【제목키워드】 COVID-19, Model, priority,