A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score

• Päätekijät: Halasz, Geza (Tekijä), Sperti, Michela (Tekijä), Villani, Matteo (Tekijä), Michelucci, Umberto (Tekijä), Agostoni, Piergiuseppe (Tekijä), Biagi, Andrea (Tekijä), Rossi, Luca (Tekijä), Botti, Andrea (Tekijä), Mari, Chiara (Tekijä), Maccarini, Marco (Tekijä), Pura, Filippo (Tekijä), Roveda, Loris (Tekijä), Nardecchia, Alessia (Tekijä), Mottola, Emanuele (Tekijä), Nolli, Massimo (Tekijä), Salvioni, Elisabetta (Tekijä), Mapelli, Massimo (Tekijä), Deriu, Marco Agostino (Tekijä), Piga, Dario (Tekijä), Piepoli, Massimo (Tekijä)
• Aineistotyyppi: Kirja
• Julkaistu: JMIR Publications, 2021-05-01T00:00:00Z.
• Aiheet: article
• Linkit: Connect to this object online.

BackgroundSeveral models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over stat...