COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients
Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval A, Sanaat A, Mostafaei S, Akhavanallaf A, Saberi A, Mansouri Z, Askari D, Ghasemian M, Sharifipour E, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, Khateri M, Bijari S, Atashzar M, Shayesteh S, Khosravi B, Babaei M, Jenabi E, Hasanian M, Shahhamzeh A, Foroghi Ghomi S, Mozafari A, Teimouri A, Movaseghi F, Ahmari A, Goharpey N, Bozorgmehr R, Shirzad-Aski H, Mortazavi R, Karimi J, Mortazavi N, Besharat S, Afsharpad M, Abdollahi H, Geramifar P, Radmard A, Arabi H, Rezaei-Kalantari K, Oveisi M, Rahmim A, Zaidi H. COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients. Computers In Biology And Medicine 2022, 145: 105467. PMID: 35378436, PMCID: PMC8964015, DOI: 10.1016/j.compbiomed.2022.105467.Peer-Reviewed Original ResearchConceptsFeature selectorArea under the receiver operating characteristic curveCT radiomics featuresDeep learning-based modelMachine learning algorithmsRadiomic featuresLearning-based modelsCOVID-19 patientsCross-validation strategyRadiomics modelLearning algorithmsSelection algorithmPrognostic modelCT-based radiomics modelRF classifierHeterogeneous datasetsHigh performanceCT radiomics modelRT-PCR positive casesReceiver operating characteristic curveTest datasetTest setDatasetLung CT radiomics featuresWhole-lung segmentation
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