Características clínicas e fatores de risco que afetam a mortalidade de crianças e adolescentes com COVID-19: um estudo de coorte retrospectivo de âmbito nacional utilizando aprendizado de máquina

• Ano de defesa: 2024
• Autor(a) principal: Adriano Lages dos Santos
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; MED - DEPARTAMENTO DE PEDIATRIA; Programa de Pós-Graduação em Ciências da Saúde - Saúde da Criança e do Adolescente; UFMG
• Palavras-chave em Português: COVID-19; Inteligência Artificial; Aprendizado de Máquina; Criança; Morte; Adolescente; Fatores de risco; Dissertação Acadêmica
• Link de acesso: http://hdl.handle.net/1843/74818; https://orcid.org/0000-0003-2351-9561

The COVID-19 pandemic has catalyzed the application of advanced digital technologies such as artificial intelligence (AI) to predict mortality in adult patients. However, the development of machine learning (ML) models for predicting outcomes in children and adolescents with COVID-19 remains limited. This study aimed to evaluate the performance of multiple machine learning models in forecasting mortality among hospitalized pediatric COVID-19 patients and assess their feasibility when applied to large-scale datasets. In this cohort study, we used the SIVEP-Gripe dataset, a public resource maintained by the Ministry of Health, to track severe acute respiratory syndrome (SARS) in Brazil. To create subsets for training and testing the machine learning (ML) models, we divided the primary dataset into three parts. Using these subsets, we developed and trained 12 ML algorithms to predict the outcomes. We assessed the performance of these models using various metrics such as accuracy, precision, sensitivity, recall, and area under the receiver operating characteristic curve (AUC). Among the 37 variables examined, 24 were found to be potential indicators of mortality, as determined by the chi-square test of independence. The LR algorithm achieved the highest performance, with an accuracy of 92.5% and an AUC of 80.1%, on the optimized dataset. GBC and ADA closely followed the LR algorithm, producing similar results. Our study also revealed that baseline reduced oxygen saturation, presence of comorbidities, and older age were the most relevant factors in predicting mortality in hospitalized children and adolescents. The use of ML models can be an asset in making clinical decisions and implementing evidence-based patient management strategies, which can enhance patient outcomes and overall quality of medical care. LR, GBC, and ADA models have demonstrated efficiency in accurately predicting mortality in COVID-19 pediatric patients.