Integration of Epidemiologic, Socioeconomic, and Sociodemographic Indicators to Predict Early COVID-19 In-Hospital Outcomes - Matos, Hetielle; Schmitt, Artur Brenner; Zeiser, Felipe André; Costa, Cristiano André da; Ramos, Gabriel de Oliveira (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2023)
The COVID-19 pandemic is an unprecedented challenge for healthcare systems around the world. In Brazil, the COVID-19 pandemic affected the population differently. Sociodemographic and socioeconomic characteristics were important indicators of early access and quality of the health system. In this way, we combine epidemiological, socioeconomic, and sociodemographic data to predict in-hospital outcomes of COVID-19. The proposed approach utilizes models such as Random Forest, XGBoost, TabNet, and CatBoost, and employs Bayesian optimization for automatic hyperparameter selection. The results demonstrate that all models exhibit a relatively higher ability to correctly identify hospital discharge outcomes than mortality cases. However, XGBoost showed the best result, with a Precision of 0.72, Recall of 0.74, F1-score of 0.64, Accuracy of 0.74, and AUC of 0.83. The quantitative and qualitative results demonstrate that our method can effectively suggest high-quality in-hospital outcomes and demonstrate the possibility of using our methodology as a tool to assist healthcare professionals.