Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Method...

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Bibliographic Details
Main Authors: Mauro Iori, Carlo Di Castelnuovo, Laura Verzellesi, Greta Meglioli, Davide Giosuè Lippolis, Andrea Nitrosi, Filippo Monelli, Giulia Besutti, Valeria Trojani, Marco Bertolini, Andrea Botti, Gastone Castellani, Daniel Remondini, Roberto Sghedoni, Stefania Croci, Carlo Salvarani
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Applied Sciences
Subjects:
machine learning
radiomics
COVID-19
X-ray radiography
under-sampling
Online Access:https://www.mdpi.com/2076-3417/12/8/3903

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