A Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic
Nenhuma Miniatura disponível
Data
2022-08-01
Autores
De Freitas, Victor Muniz
Chiloff, Daniela Mendes
Bosso, Giulia Gabriella
Teixeira, Janaina Oliveira Pires
Hernandes, Isabele Cristina de Godói
Padilha, Maira do Patrocínio
Moura, Giovanna Corrêa
De Andrade, Luis Gustavo Modelli [UNESP]
Mancuso, Frederico
Finamor, Francisco Estivallet
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
A machine learning approach is a useful tool for risk-stratifying patients with respiratory symptoms during the COVID-19 pandemic, as it is still evolving. We aimed to verify the predictive capacity of a gradient boosting decision trees (XGboost) algorithm to select the most important predictors including clinical and demographic parameters in patients who sought medical support due to respiratory signs and symptoms (RAPID RISK COVID-19). A total of 7336 patients were enrolled in the study, including 6596 patients that did not require hospitalization and 740 that required hospitalization. We identified that patients with respiratory signs and symptoms, in particular, lower oxyhemoglobin saturation by pulse oximetry (SpO2) and higher respiratory rate, fever, higher heart rate, and lower levels of blood pressure, associated with age, male sex, and the underlying conditions of diabetes mellitus and hypertension, required hospitalization more often. The predictive model yielded a ROC curve with an area under the curve (AUC) of 0.9181 (95% CI, 0.9001 to 0.9361). In conclusion, our model had a high discriminatory value which enabled the identification of a clinical and demographic profile predictive, preventive, and personalized of COVID-19 severity symptoms.
Descrição
Palavras-chave
COVID-19, hospitalization, machine learning, predictive model
Como citar
Journal of Clinical Medicine, v. 11, n. 15, 2022.