Machine learning for prediction of in-hospital mortality in coronavirus disease 2019 patients: results from an Italian multicenter study
Articolo
Data di Pubblicazione:
2022
Abstract:
Several risk factors have been identified to predict worse outcomes in patients affected by SARS-CoV-2 infection. Machine learning algorithms represent a novel approach to identifying a prediction model with a good discriminatory capacity to be easily used in clinical practice. The aim of this study was to obtain a risk score for in-hospital mortality in patients with coronavirus disease infection (COVID-19) based on a limited number of features collected at hospital admission.
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
Aged; Aged, 80 and over; Creatinine; Female; Hospital Mortality; Humans; Machine Learning; Male; Middle Aged; SARS-CoV-2; Troponin; COVID-19
Elenco autori:
Vezzoli, Marika; Inciardi, Riccardo Maria; Oriecuia, Chiara; Paris, Sara; Murillo, Natalia Herrera; Agostoni, Piergiuseppe; Ameri, Pietro; Bellasi, Antonio; Camporotondo, Rita; Canale, Claudia; Carubelli, Valentina; Carugo, Stefano; Catagnano, Francesco; Danzi, Giambattista; Dalla Vecchia, Laura; Giovinazzo, Stefano; Gnecchi, Massimiliano; Guazzi, Marco; Iorio, Anita; La Rovere, Maria Teresa; Leonardi, Sergio; Maccagni, Gloria; Mapelli, Massimo; Margonato, Davide; Merlo, Marco; Monzo, Luca; Mortara, Andrea; Nuzzi, Vincenzo; Pagnesi, Matteo; Piepoli, Massimo; Porto, Italo; Pozzi, Andrea; Provenzale, Giovanni; Sarullo, Filippo; Senni, Michele; Sinagra, Gianfranco; Tomasoni, Daniela; Adamo, Marianna; Volterrani, Maurizio; Maroldi, Roberto; Metra, Marco; Lombardi, Carlo Mario; Specchia, Claudia
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