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.
Machine learning for prediction of in-hospital mortality in coronavirus disease 2019 patients: results from an Italian multicenter study / M. Vezzoli, R.M. Inciardi, C. Oriecuia, S. Paris, N.H. Murillo, P. Agostoni, P. Ameri, A. Bellasi, R. Camporotondo, C. Canale, V. Carubelli, S. Carugo, F. Catagnano, G. Danzi, L. Dalla Vecchia, S. Giovinazzo, M. Gnecchi, M. Guazzi, A. Iorio, M.T. La Rovere, S. Leonardi, G. Maccagni, M. Mapelli, D. Margonato, M. Merlo, L. Monzo, A. Mortara, V. Nuzzi, M. Pagnesi, M. Piepoli, I. Porto, A. Pozzi, G. Provenzale, F. Sarullo, M. Senni, G. Sinagra, D. Tomasoni, M. Adamo, M. Volterrani, R. Maroldi, M. Metra, C.M. Lombardi, C. Specchia. - In: JOURNAL OF CARDIOVASCULAR MEDICINE. - ISSN 1558-2027. - 23:7(2022 Jul 01), pp. 439-446. [10.2459/JCM.0000000000001329]
Machine learning for prediction of in-hospital mortality in coronavirus disease 2019 patients: results from an Italian multicenter study
P. Agostoni;A. Bellasi;S. Carugo;M. Guazzi;M. Mapelli;M. Piepoli;G. Provenzale;
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.
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