A Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic.

Abstract

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.

• Authors: Victor Muniz De Freitas, Daniela Mendes Chiloff, Giulia Gabriella Bosso, Janaina Oliveira Pires Teixeira, Isabele Cristina de Godói Hernandes, Maira do Patrocínio Padilha, Giovanna Corrêa Moura, Luis Gustavo Modelli De Andrade, Frederico Mancuso, Francisco Estivallet Finamor, Aluísio Marçal de Barros Serodio, Jaquelina Sonoe Ota Arakaki, Marair Gracio Ferreira Sartori, Paulo Roberto Abrão Ferreira, Érika Bevilaqua Rangel
• Journal: Journal of clinical medicine (J Clin Med) Vol. 11 Issue 15 (Aug 05 2022) ISSN: 2077-0383 [Print] Switzerland
• PMID: 35956189 (Publication Type: Journal Article)