In Journal of integrative bioinformatics
To support physicians in clinical decision process on patients affected by Coronavirus Disease 2019 (COVID-19) in areas with a low vaccination rate, we devised and evaluated the performances of several machine learning (ML) classifiers fed with readily available clinical and laboratory data. Our observational retrospective study collected data from a cohort of 779 COVID-19 patients presenting to three hospitals of the Lazio-Abruzzo area (Italy). Based on a different selection of clinical and respiratory (ROX index and PaO2/FiO2 ratio) variables, we devised an AI-driven tool to predict safe discharge from ED, disease severity and mortality during hospitalization. To predict safe discharge our best classifier is an RF integrated with ROX index that reached AUC of 0.96. To predict disease severity the best classifier was an RF integrated with ROX index that reached an AUC of 0.91. For mortality prediction the best classifier was an RF integrated with ROX index, that reached an AUC of 0.91. The results obtained thanks to our algorithms are consistent with the scientific literature an accomplish significant performances to forecast safe discharge from ED and severe clinical course of COVID-19.
Casano Nicolò, Santini Silvano Junior, Vittorini Pierpaolo, Sinatti Gaia, Carducci Paolo, Mastroianni Claudio Maria, Ciardi Maria Rosa, Pasculli Patrizia, Petrucci Emiliano, Marinangeli Franco, Balsano Clara
2023-Mar-07
COVID-19, ROX index, SARS-CoV-2, emergency medicine, machine learning
