2021
DOI: 10.3390/jpm11060501
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Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

Abstract: 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 in… Show more

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Cited by 23 publications
(27 citation statements)
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References 59 publications
(105 reference statements)
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“…The nine training subgroups were then combined, leaving one training and one testing cohort. The training and testing datasets were constructed using a pure random 10-fold cross-validation [38]. The following statistical classification measures were used for the evaluations: precision, recall, F1 score, true positive rate (TPR), false positive rate (FPR), area under the receiver operating characteristic curve (AUC), accuracy [33], and average success rate of classification [36].…”
Section: Statistical Analyses and Machine Learning Frameworkmentioning
confidence: 99%
“…The nine training subgroups were then combined, leaving one training and one testing cohort. The training and testing datasets were constructed using a pure random 10-fold cross-validation [38]. The following statistical classification measures were used for the evaluations: precision, recall, F1 score, true positive rate (TPR), false positive rate (FPR), area under the receiver operating characteristic curve (AUC), accuracy [33], and average success rate of classification [36].…”
Section: Statistical Analyses and Machine Learning Frameworkmentioning
confidence: 99%
“…The frequent involvement of the pulmonary circulation in COVID-19, with diffuse pathophysiologic drivers of hypoxemia, capillary microthrombosis, and thromboembolism [ 4 , 35 ], has been extensively described. At CT scan, vascular alterations are seen as vascular thickening, pulmonary artery enlargement, and vascular congestion [ 36 , 37 ].…”
Section: Computed Tomographymentioning
confidence: 99%
“…The model successfully predicted the risk of progression on the third, fifth, and seventh day, which suggested that a multimodal combination of CT radiomics characteristics and clinical variables could predict the severity and progression of COVID-19 to critical illness with high accuracy. Moreover, Schaffino et al [43] used machine learning models to assess lung parenchyma and vascular damage. Their results showed that the best models of SVM and MLP considered the same ten input features, yielding an AUC of 0.747 and 0.844, respectively.…”
Section: Constructing Image Multimodal Models To Evaluate Covid-19mentioning
confidence: 99%