2021/05/13 by Matteo Chieregato, M. Chieregato, Chieregato, Matteo +15
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Medicine · Physics and Astronomy · #Artificial intelligence #COVID-19 diagnosis using AI #Classifier (UML) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deep learning #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Feature selection #Gradient boosting #Image and Video Processing (eess.IV) #Intensive care #Intensive care medicine #Machine Learning (cs.LG) #Machine Learning in Healthcare #Machine learning #Medical Physics (physics.med-ph) #Medicine #Naive Bayes classifier #Probabilistic classification #Quantitative Methods (q-bio.QM) #Radiomics and Machine Learning in Medical Imaging #Random forest #Support vector machine #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering #physics.med-ph #q-bio.QM
paper · pdf · doi:10.48550/arxiv.2105.06141
published in arXiv (Cornell University) (Cornell University) · 16 pages, 10 figures, 2 supplementary tables
arxiv created 2021/05/13 · openalex publication_date 2021/05/13 · arxiv updated 2021/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for selection in a Boruta algorithm with SHAP game theoretical values. A classifier is built on the reduced feature space using CatBoost gradient boosting algorithm and reaching a probabilistic AUC of 0.949 on holdout test set. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.