RT Journal Article
T1 Age-Stratified Analysis of COVID-19 Outcome Using Machine Learning Predictive Models
A1 Domínguez Olmedo, Juan Luis
A1 Gragera Martínez, Álvaro
A1 Mata Vázquez, Jacinto
A1 Pachón Álvarez, Victoria
AB Since the emergence of COVID-19, most health systems around the world have experienced a series of spikes in the number of infected patients, leading to collapse of the health systems in many countries. The use of clinical laboratory tests can serve as a discriminatory method for disease severity, defining the profile of patients with a higher risk of mortality. In this paper, we study the results of applying predictive models to data regarding COVID-19 outcome, using three datasets after age stratification of patients. The extreme gradient boosting (XGBoost) algorithm was employed as the predictive method, yielding excellent results. The area under the receiving operator characteristic curve (AUROC) value was 0.97 for the subgroup of patients up to 65 years of age. In addition, SHAP (Shapley additive explanations) was used to analyze the feature importance in the resulting models
PB MDPI
SN 2227-9032 (electrónico)
YR 2022
FD 2022-10
LK https://hdl.handle.net/10272/21567
UL https://hdl.handle.net/10272/21567
LA eng
NO Domínguez-Olmedo, J. L., Gragera-Martínez, Á., Mata, J., & Pachón, V. (2022). Age-Stratified Analysis of COVID-19 Outcome Using Machine Learning Predictive Models. In Healthcare (Vol. 10, Issue 10, p. 2027). MDPI AG. https://doi.org/10.3390/healthcare10102027
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026