RT Journal Article
T1 Age-Stratified Analysis of COVID-19 Outcome Using Machine Learning Predictive Models.
A1 Domínguez-Olmedo, Juan L
A1 Gragera-Martínez, Álvaro
A1 Mata, Jacinto
A1 Pachón, Victoria
K1 COVID-19
K1 feature importance
K1 machine learning
K1 prediction
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.
SN 2227-9032
YR 2022
FD 2022-10-14
LK http://hdl.handle.net/10668/21005
UL http://hdl.handle.net/10668/21005
LA en
DS RISalud
RD Apr 9, 2025