FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4294085647> ?p ?o ?g. }

• W4294085647 abstract "We aimed to construct a prediction model based on computed tomography (CT) radiomics features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 patients were studied from a publicly available dataset with 4-class severity scoring performed by a radiologist (based on CT images and clinical features). The entire lungs were segmented and followed by resizing, bin discretization and radiomic features extraction. We utilized two feature selection algorithms, namely bagging random forest (BRF) and multivariate adaptive regression splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. The dataset was divided into 50% (555 samples), 20% (223 samples), and 30% (332 samples) for training, validation, and untouched test datasets, respectively. Subsequently, nested cross-validation was performed on train/validation to select the features and tune the models. All predictive power indices were reported based on the testing set. The performance of multi-class models was assessed using precision, recall, F1-score, and accuracy based on the 4 × 4 confusion matrices. In addition, the areas under the receiver operating characteristic curves (AUCs) for multi-class classifications were calculated and compared for both models. Using BRF, 23 radiomic features were selected, 11 from first-order, 9 from GLCM, 1 GLRLM, 1 from GLDM, and 1 from shape. Ten features were selected using the MARS algorithm, namely 3 from first-order, 1 from GLDM, 1 from GLRLM, 1 from GLSZM, 1 from shape, and 3 from GLCM features. The mean absolute deviation, skewness, and variance from first-order and flatness from shape, and cluster prominence from GLCM features and Gray Level Non Uniformity Normalize from GLRLM were selected by both BRF and MARS algorithms. All selected features by BRF or MARS were significantly associated with four-class outcomes as assessed within MLR (All p values < 0.05). BRF + MLR and MARS + MLR resulted in pseudo-R2 prediction performances of 0.305 and 0.253, respectively. Meanwhile, there was a significant difference between the feature selection models when using a likelihood ratio test (p value = 0.046). Based on confusion matrices for BRF + MLR and MARS + MLR algorithms, the precision was 0.856 and 0.728, the recall was 0.852 and 0.722, whereas the accuracy was 0.921 and 0.861, respectively. AUCs (95% CI) for multi-class classification were 0.846 (0.805-0.887) and 0.807 (0.752-0.861) for BRF + MLR and MARS + MLR algorithms, respectively. Our models based on the utilization of radiomic features, coupled with machine learning were able to accurately classify patients according to the severity of pneumonia, thus highlighting the potential of this emerging paradigm in the prognostication and management of COVID-19 patients." @default.
• W4294085647 created "2022-09-01" @default.
• W4294085647 creator A5002885963 @default.
• W4294085647 creator A5003433949 @default.
• W4294085647 creator A5007891293 @default.
• W4294085647 creator A5021438906 @default.
• W4294085647 creator A5034625862 @default.
• W4294085647 creator A5036836472 @default.
• W4294085647 creator A5039181443 @default.
• W4294085647 creator A5048817483 @default.
• W4294085647 creator A5069570780 @default.
• W4294085647 date "2022-09-01" @default.
• W4294085647 modified "2023-10-05" @default.
• W4294085647 title "High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms" @default.
• W4294085647 cites W1971829180 @default.
• W4294085647 cites W1981818023 @default.
• W4294085647 cites W2066898336 @default.
• W4294085647 cites W2082575119 @default.
• W4294085647 cites W2087676836 @default.
• W4294085647 cites W2158639519 @default.
• W4294085647 cites W2174661749 @default.
• W4294085647 cites W2293034341 @default.
• W4294085647 cites W2409649574 @default.
• W4294085647 cites W2462062777 @default.
• W4294085647 cites W2729995019 @default.
• W4294085647 cites W2767128594 @default.
• W4294085647 cites W2917364154 @default.
• W4294085647 cites W2998789541 @default.
• W4294085647 cites W3001195213 @default.
• W4294085647 cites W3009875419 @default.
• W4294085647 cites W3010061930 @default.
• W4294085647 cites W3010278110 @default.
• W4294085647 cites W3011048075 @default.
• W4294085647 cites W3012751338 @default.
• W4294085647 cites W3015696390 @default.
• W4294085647 cites W3019336217 @default.
• W4294085647 cites W3026637813 @default.
• W4294085647 cites W3034951140 @default.
• W4294085647 cites W3035151116 @default.
• W4294085647 cites W3037424146 @default.
• W4294085647 cites W3037848912 @default.
• W4294085647 cites W3040075864 @default.
• W4294085647 cites W3044185109 @default.
• W4294085647 cites W3048366290 @default.
• W4294085647 cites W3080709651 @default.
• W4294085647 cites W3084067637 @default.
• W4294085647 cites W3086849853 @default.
• W4294085647 cites W3089992861 @default.
• W4294085647 cites W3091940685 @default.
• W4294085647 cites W3092314636 @default.
• W4294085647 cites W3108203735 @default.
• W4294085647 cites W3108802735 @default.
• W4294085647 cites W3135079895 @default.
• W4294085647 cites W3135092238 @default.
• W4294085647 cites W3188071779 @default.
• W4294085647 cites W3189257822 @default.
• W4294085647 cites W3192156946 @default.
• W4294085647 cites W3194507152 @default.
• W4294085647 cites W3197767853 @default.
• W4294085647 cites W3201539940 @default.
• W4294085647 cites W3204456443 @default.
• W4294085647 cites W3207052288 @default.
• W4294085647 cites W3208239477 @default.
• W4294085647 cites W3211291791 @default.
• W4294085647 cites W3212441610 @default.
• W4294085647 cites W4200237742 @default.
• W4294085647 cites W4200250907 @default.
• W4294085647 cites W4200427670 @default.
• W4294085647 cites W4205334579 @default.
• W4294085647 cites W4225080248 @default.
• W4294085647 cites W4226059105 @default.
• W4294085647 cites W4226439930 @default.
• W4294085647 cites W4282979895 @default.
• W4294085647 cites W4283030324 @default.
• W4294085647 cites W4292658234 @default.
• W4294085647 doi "https://doi.org/10.1038/s41598-022-18994-z" @default.
• W4294085647 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36050434" @default.
• W4294085647 hasPublicationYear "2022" @default.
• W4294085647 type Work @default.
• W4294085647 citedByCount "11" @default.
• W4294085647 countsByYear W42940856472021 @default.
• W4294085647 countsByYear W42940856472022 @default.
• W4294085647 countsByYear W42940856472023 @default.
• W4294085647 crossrefType "journal-article" @default.
• W4294085647 hasAuthorship W4294085647A5002885963 @default.
• W4294085647 hasAuthorship W4294085647A5003433949 @default.
• W4294085647 hasAuthorship W4294085647A5007891293 @default.
• W4294085647 hasAuthorship W4294085647A5021438906 @default.
• W4294085647 hasAuthorship W4294085647A5034625862 @default.
• W4294085647 hasAuthorship W4294085647A5036836472 @default.
• W4294085647 hasAuthorship W4294085647A5039181443 @default.
• W4294085647 hasAuthorship W4294085647A5048817483 @default.
• W4294085647 hasAuthorship W4294085647A5069570780 @default.
• W4294085647 hasBestOaLocation W42940856471 @default.
• W4294085647 hasConcept C119857082 @default.
• W4294085647 hasConcept C148483581 @default.
• W4294085647 hasConcept C153180895 @default.
• W4294085647 hasConcept C154945302 @default.
• W4294085647 hasConcept C169258074 @default.
• W4294085647 hasConcept C169903167 @default.

• next