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

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.
2022
Pubblicato
https://journals.lww.com/jcardiovascularmedicine/Fulltext/2022/07000/Machine_learning_for_prediction_of_in_hospital.4.aspx#ej-article-sam-container
File in questo prodotto:
File Dimensione Formato  
Machine_learning_for_prediction_of_in_hospital.4.pdf

Accesso chiuso

Tipologia: Documento in Versione Editoriale
Licenza: Digital Rights Management non definito
Dimensione 831.45 kB
Formato Adobe PDF
831.45 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
jcm_2022_05_02_lombardi_jcm-d-21-00942_sdc1.pdf

Accesso chiuso

Tipologia: Altro materiale allegato
Licenza: Digital Rights Management non definito
Dimensione 413.25 kB
Formato Adobe PDF
413.25 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Machine_learning_for_prediction_of_in_hospital.4-Post_print.pdf

Open Access dal 01/08/2023

Tipologia: Bozza finale post-referaggio (post-print)
Licenza: Creative commons
Dimensione 830.71 kB
Formato Adobe PDF
830.71 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3038979
Citazioni
  • ???jsp.display-item.citation.pmc??? 3
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 6
social impact