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

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Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorDe Freitas, Victor Muniz-
Autor(es): dc.creatorChiloff, Daniela Mendes-
Autor(es): dc.creatorBosso, Giulia Gabriella-
Autor(es): dc.creatorTeixeira, Janaina Oliveira Pires-
Autor(es): dc.creatorHernandes, Isabele Cristina de Godói-
Autor(es): dc.creatorPadilha, Maira do Patrocínio-
Autor(es): dc.creatorMoura, Giovanna Corrêa-
Autor(es): dc.creatorDe Andrade, Luis Gustavo Modelli-
Autor(es): dc.creatorMancuso, Frederico-
Autor(es): dc.creatorFinamor, Francisco Estivallet-
Autor(es): dc.creatorSerodio, Aluísio Marçal de Barros-
Autor(es): dc.creatorArakaki, Jaquelina Sonoe Ota-
Autor(es): dc.creatorSartori, Marair Gracio Ferreira-
Autor(es): dc.creatorFerreira, Paulo Roberto Abrão-
Autor(es): dc.creatorRangel, Érika Bevilaqua-
Data de aceite: dc.date.accessioned2025-08-21T21:50:10Z-
Data de disponibilização: dc.date.available2025-08-21T21:50:10Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2022-08-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/jcm11154574-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241557-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241557-
Descrição: dc.descriptionA 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: dc.descriptionPaulista School of Medicine Hospital São Paulo Federal University of São Paulo-
Descrição: dc.descriptionDepartment of Internal Medicine Botucatu Medical School University of São Paulo State-
Descrição: dc.descriptionDiscipline of Emergency Medicine Department of Medicine Paulista School of Medicine Hospital São Paulo Federal University of São Paulo-
Descrição: dc.descriptionSector of Bioethics Department of Surgery Paulista School of Medicine Hospital São Paulo Federal University of São Paulo-
Descrição: dc.descriptionPneumology Division Department of Medicine Paulista School of Medicine Hospital São Paulo Federal University of São Paulo-
Descrição: dc.descriptionDepartment of Obstetrics Paulista School of Medicine Hospital São Paulo Federal University of São Paulo-
Descrição: dc.descriptionInfectious Disease Division Department of Medicine Paulista School of Medicine Hospital São Paulo Federal University of São Paulo-
Descrição: dc.descriptionNephrology Division Department of Medicine Paulista School of Medicine Hospital São Paulo Federal University of São Paulo-
Descrição: dc.descriptionDepartment of Internal Medicine Botucatu Medical School University of São Paulo State-
Idioma: dc.languageen-
Relação: dc.relationJournal of Clinical Medicine-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCOVID-19-
Palavras-chave: dc.subjecthospitalization-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectpredictive model-
Título: dc.titleA Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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