[Source: Patterns, full page: (LINK). Abstract, edited.]
A Learning-based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics
Yichao Zheng, Yinheng Zhu, Mengqi Ji, Rongpin Wang, Xinfeng Liu, Mudan Zhang, Choo Hui Qin, Lu Fang, Shaohua Ma
Open Access | Published: August 03, 2020 | DOI: https://doi.org/10.1016/j.patter.2020.100092
- A model was developed to evaluate hospitalization priority in COVID-19 pandemics.
- This model used easily accessible biomarkers to evaluate the risk of severe COVID-19.
- The evaluation can be rapidly proceeded using an online program.
- Performance of different algorithms in evaluation of COVID-19 severity was explored.
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.
Accepted: July 29, 2020 – Received in revised form: June 30, 2020 – Received: May 12, 2020
Publication stage In Press Accepted Manuscript
Identification DOI: https://doi.org/10.1016/j.patter.2020.100092
Copyright © 2020
Keywords: SARS-CoV-2; COVID-19.