2022
Dataset on acute stroke risk stratification from CT angiographic radiomics
Avery EW, Behland J, Mak A, Haider SP, Zeevi T, Sanelli PC, Filippi CG, Malhotra A, Matouk CC, Griessenauer CJ, Zand R, Hendrix P, Abedi V, Falcone GJ, Petersen N, Sansing LH, Sheth KN, Payabvash S. Dataset on acute stroke risk stratification from CT angiographic radiomics. Data In Brief 2022, 44: 108542. PMID: 36060820, PMCID: PMC9428796, DOI: 10.1016/j.dib.2022.108542.Peer-Reviewed Original ResearchMachine Learning FrameworkImage processing technologyFeature selection algorithmField of radiomicsRadiomics-based analysisMachine learningMedical imagesSelection algorithmAssistance toolRadiomic featuresRadiomics dataProcessing technologyAnalysis frameworkRelevant informationRadiomics algorithmAlgorithmCT angiography imagesRadiomicsMethodological supportExternal testingFrameworkImagesAngiography imagesMachineFeaturesCOVID-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 segmentationVariable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods
Du J, Boss J, Han P, Beesley L, Kleinsasser M, Goutman S, Batterman S, Feldman E, Mukherjee B. Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods. Journal Of Computational And Graphical Statistics 2022, 31: 1063-1075. PMID: 36644406, PMCID: PMC9838615, DOI: 10.1080/10618600.2022.2035739.Peer-Reviewed Original ResearchVariable selectionSimultaneous coefficient estimationPenalized regression methodsBinary outcome dataObjective functionR-package <i>Shrinkage penaltyGeneral classCyclic coordinate descentVariable selection algorithmCoefficient estimatesSupplementary materialsMethod to dataCoordinate descentMultiple imputationALS riskMultiply-imputedOutcome dataFunction formulationSelectivity propertiesSelection algorithmEstimationOptimization algorithmMissingnessBiomedical applications
2016
ADHD and ASD Classification Based on Emotion Recognition Data
Uluvazmur-Ozturk M, Arman A, Yilmaz S, Findik O, Genc H, Carkaxhiu-Bulut G, Yazgan M, Teker U, Cataltepe Z. ADHD and ASD Classification Based on Emotion Recognition Data. 2016, 810-813. DOI: 10.1109/icmla.2016.0145.Peer-Reviewed Original ResearchAttention deficit hyperactivity disorderAutism spectrum disorderEmotion recognition experimentsDeficit hyperactivity disorderImages of facesReliefF feature selection algorithmCertain emotionsHyperactivity disorderSpectrum disorderASD classificationResponse latencyRecognition dataRecognition experimentsEmotionsFeature selection algorithmParticipantsRelevant imagesClassification problemClassification algorithmsClassification processClassification stepFeature selectionClassification performanceSelection algorithmExperiment environment
2009
Optimization of Electrode Channels in Brain Computer Interfaces
Kamrunnahar M, Dias N, Schiff S. Optimization of Electrode Channels in Brain Computer Interfaces. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2009, 2009: 6477-6480. PMID: 19964437, PMCID: PMC2859435, DOI: 10.1109/iembs.2009.5333585.Peer-Reviewed Original ResearchConceptsBrain-computer interfaceFeature classificationMotor imagery tasksComputer interfaceBCI applicationsLinear discriminant analysisFeature selection algorithmFeature selection techniquesStepwise feature selectionFeature selectionSelection algorithmSelection techniquesImagery tasksOptimization approachOptimal numberTaskDiscrimination errorsHuman scalp electroencephalographyChannel combinationsElectrode channelsClassificationOptimum numberReliable selection
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply