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: Bertolini, M. (Author), Besutti, G. (Author), Botti, A. (Author), Castellani, G. (Author), Croci, S. (Author), Di Castelnuovo, C. (Author), Iori, M. (Author), Lippolis, D.G (Author), Meglioli, G. (Author), Monelli, F. (Author), Nitrosi, A. (Author), Remondini, D. (Author), Salvarani, C. (Author), Sghedoni, R. (Author), Trojani, V. (Author), Verzellesi, L. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
COVID-19
machine learning
radiomics
under-sampling
X-ray radiography
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