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 / Vezzoli, M., Inciardi, R.M., Oriecuia, C., Paris, S., Murillo, N.H., Agostoni, P., Ameri, P., Bellasi, A., Camporotondo, R., Canale, C., Carubelli, V., Carugo, S., Catagnano, F., Danzi, G., Dalla Vecchia, L., Giovinazzo, S., Gnecchi, M., Guazzi, M., Iorio, A., La Rovere, M.T., et al.. - In: JOURNAL OF CARDIOVASCULAR MEDICINE. - ISSN 1558-2035. - ELETTRONICO. - 23:7(2022), 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
Merlo, Marco;Nuzzi, Vincenzo;Sinagra, Gianfranco;
2022-01-01
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.| File | Dimensione | Formato | |
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