Outcome Prediction for SARS-CoV-2 Patients Using Machine Learning Modeling of Clinical, Radiological, and Radiomic Features Derived from Chest CT Images

Outcome Prediction for SARS-CoV-2 Patients Using Machine Learning Modeling of Clinical, Radiological, and Radiomic Features Derived from Chest CT Images

(1) Background: Chest Computed Tomography (CT) has been proposed as a non-invasive method for confirming the diagnosis of SARS-CoV-2 patients using radiomic features (RFs) and baseline clinical data. The performance of Machine Learning (ML) methods using RFs derived from semi-automatically segmented...

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Bibliographic Details
Main Authors: Lorenzo Spagnoli, Maria Francesca Morrone, Enrico Giampieri, Giulia Paolani, Miriam Santoro, Nico Curti, Francesca Coppola, Federica Ciccarese, Giulio Vara, Nicolò Brandi, Rita Golfieri, Michele Bartoletti, Pierluigi Viale, Lidia Strigari
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Applied Sciences
Subjects:
radiomics
CT images
Machine Learning
SARS-CoV-2
mortality
Online Access:https://www.mdpi.com/2076-3417/12/9/4493

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https://www.mdpi.com/2076-3417/12/9/4493

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