Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive mod...

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Bibliographic Details
Main Authors: Simone Schiaffino, Marina Codari, Andrea Cozzi, Domenico Albano, Marco Alì, Roberto Arioli, Emanuele Avola, Claudio Bnà, Maurizio Cariati, Serena Carriero, Massimo Cressoni, Pietro S. C. Danna, Gianmarco Della Pepa, Giovanni Di Leo, Francesco Dolci, Zeno Falaschi, Nicola Flor, Riccardo A. Foà, Salvatore Gitto, Giovanni Leati, Veronica Magni, Alexis E. Malavazos, Giovanni Mauri, Carmelo Messina, Lorenzo Monfardini, Alessio Paschè, Filippo Pesapane, Luca M. Sconfienza, Francesco Secchi, Edoardo Segalini, Angelo Spinazzola, Valeria Tombini, Silvia Tresoldi, Angelo Vanzulli, Ilaria Vicentin, Domenico Zagaria, Dominik Fleischmann, Francesco Sardanelli
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Journal of Personalized Medicine
Subjects:
COVID-19
lung
pulmonary artery
tomography
X-ray computed
machine learning
Online Access:https://www.mdpi.com/2075-4426/11/6/501

Internet

https://www.mdpi.com/2075-4426/11/6/501

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