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AUTHOR

Riccardo Foà

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Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

2021

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann–Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset spli…

Medicine (miscellaneous)X-ray computedtomography030204 cardiovascular system & hematologyMachine learningcomputer.software_genreArticlelung030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicinepulmonary arterymedicine.arterymedicinesupport vector machinecomputerUnivariate analysisLungbusiness.industryRArea under the curveCOVID-19Emergency departmentneural networksmachine learningmedicine.anatomical_structureRadiological weaponPulmonary arteryMann–Whitney U testMedicineprognosisArtificial intelligenceTomographybusinesscomputerJournal of Personalized Medicine
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