2024
Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography
Tran A, Desser D, Zeevi T, Karam G, Zietz J, Dell’Orco A, Chen M, Malhotra A, Qureshi A, Murthy S, Majidi S, Falcone G, Sheth K, Nawabi J, Payabvash S. Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography. Applied Sciences 2024, 15: 111. PMID: 40046237, PMCID: PMC11882137, DOI: 10.3390/app15010111.Peer-Reviewed Original ResearchIntracerebral hemorrhageHematoma expansionFollow-up CT scansFollow-up head computed tomographyPredictors of poor outcomeDeep learning classification modelFollow-up scansHead computed tomographyFalse-negative resultsHematoma segmentationAutomated segmentationMulticentre cohortCT scanValidation cohortPoor outcomeComputed tomographyFollow-upClassification modelOptimizational methodHematomaAnnotationGeneration of Multimodal Longitudinal Synthetic Data By Artificial Intelligence to Improve Personalized Medicine in Hematology
D'Amico S, Delleani M, Sauta E, Asti G, Zazzetti E, Campagna A, Lanino L, Maggioni G, Ubezio M, Todisco G, Russo A, Tentori C, Buizza A, Bicchieri M, Zampini M, Brindisi M, Ficara F, Riva E, Ventura D, Crisafulli L, Pinocchio N, Jacobs F, Zambelli A, Savevski V, Santoro A, Sanavia T, Rollo C, Sartori F, Fariselli P, Sanz G, Santini V, Sole F, Platzbecker U, Fenaux P, Diez-Campelo M, Kordasti S, Komrokji R, Garcia-Manero G, Haferlach T, Zeidan A, Castellani G, Della Porta M. Generation of Multimodal Longitudinal Synthetic Data By Artificial Intelligence to Improve Personalized Medicine in Hematology. Blood 2024, 144: 4981-4981. DOI: 10.1182/blood-2024-209541.Peer-Reviewed Original ResearchDeep learning-based frameworkLearning-based frameworkPrivacy preservationSynthetic dataSynthetic patientsPerformance of classificationXGBoost classification modelDisease classificationPrivacy-compliantConditional GANPrivacy protectionPrivacy risksLanguage modelGeneration pipelineMultimodal featuresFeature distributionGenerative AIMultimodal dataModel trainingMosaic frameworkClassification modelData integrationArtificial intelligencePrivacyClipping moduleEnhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning
Liang L, Wang Y, Ma H, Zhang R, Liu R, Zhu R, Zheng Z, Zhang X, Wang F. Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning. Frontiers In Psychiatry 2024, 15: 1422020. PMID: 39355380, PMCID: PMC11442283, DOI: 10.3389/fpsyt.2024.1422020.Peer-Reviewed Original ResearchVocal acoustic featuresHealthy control groupSeverity of depressive symptomsTotal depression scoreAcoustic featuresClassification accuracyMDD groupDepressive disorderAnxiety comorbiditiesDepression prediction modelDeep learning methodsDepressive symptomsDepression scoresHC groupSpeech characteristicsMean Absolute Error(MAEDepressionNeural networkEnhanced classificationControl groupLearning methodsMachine learningClassification modelOpen-source algorithmAbsolute error(MAESIFT-DBT: Self-Supervised Initialization and Fine-Tuning for Imbalanced Digital Breast Tomosynthesis Image Classification
Du Y, Hooley R, Lewin J, Dvornek N. SIFT-DBT: Self-Supervised Initialization and Fine-Tuning for Imbalanced Digital Breast Tomosynthesis Image Classification. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2024, 00: 1-5. PMID: 39263046, PMCID: PMC11386909, DOI: 10.1109/isbi56570.2024.10635723.Peer-Reviewed Original Research
2023
Suicide Tendency Prediction from Psychiatric Notes Using Transformer Models
Li Z, Ameer I, Hu Y, Abdelhameed A, Tao C, Selek S, Xu H. Suicide Tendency Prediction from Psychiatric Notes Using Transformer Models. 2023, 00: 481-483. DOI: 10.1109/ichi57859.2023.00074.Peer-Reviewed Original ResearchWeighted F1 scoreF1 scoreMachine learning modelsElectronic health recordsLearning modelsState-of-the-art modelsState-of-the-artBinary classification taskHealth recordsBinary classification modelStandard diagnosis codesClassification taskMulticlass classificationHealth informaticsClassification modelMental health informaticsTransformation modelPrediction algorithmPsychiatric notesInitial psychiatric evaluationSuicidal tendenciesMachineRandom forest modelSuicidal ideationPerformanceMulti-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG
Yan W, Yu L, Liu D, Sui J, Calhoun V, Lin Z. Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG. Frontiers In Psychiatry 2023, 14: 1202049. PMID: 37441141, PMCID: PMC10333510, DOI: 10.3389/fpsyt.2023.1202049.Peer-Reviewed Original ResearchConvolutional recurrent neural networkRecurrent neural networkResting-state EEGNeural networkPsychiatric disordersDeep learning classification modelLow-dimensional subspaceTwo-class classificationDesigning individualized treatmentLearning classification modelsEEG backgroundClassification modelHealthy controlsDepressive disorderSpatiotemporal informationClinical observationsDisease severityAccurate classificationIndividualized treatmentBiomarkersDisorder classificationDisorder identificationDisordersClassificationNeuroimaging biomarkersMulti-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study
Meng X, Iraji A, Fu Z, Kochunov P, Belger A, Ford J, McEwen S, Mathalon D, Mueller B, Pearlson G, Potkin S, Preda A, Turner J, van Erp T, Sui J, Calhoun V. Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study. NeuroImage Clinical 2023, 38: 103434. PMID: 37209635, PMCID: PMC10209454, DOI: 10.1016/j.nicl.2023.103434.Peer-Reviewed Original ResearchConceptsIndependent component analysisData-driven approachData miningF1 scoreClassification modelReference algorithmNetwork connectivityMagnetic resonance imaging dataNetworkImaging dataPredictive resultsPatient dataFunctional magnetic resonance imaging (fMRI) dataData acquisition timeConnectivity networksFrameworkConnectivityPromising approachNew subjectMiningAnalytic approachAlgorithmDatasetAcquisition timeComponent analysis
2022
Identifying Medicare Beneficiaries With Delirium
Moura LMVR, Zafar S, Benson NM, Festa N, Price M, Donahue MA, Normand SL, Newhouse JP, Blacker D, Hsu J. Identifying Medicare Beneficiaries With Delirium. Medical Care 2022, 60: 852-859. PMID: 36043702, PMCID: PMC9588515, DOI: 10.1097/mlr.0000000000001767.Peer-Reviewed Original Research
2019
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella G, Hanna M, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam K, Brogi E, Reuter V, Klimstra D, Fuchs T. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine 2019, 25: 1301-1309. PMID: 31308507, PMCID: PMC7418463, DOI: 10.1038/s41591-019-0508-1.Peer-Reviewed Original ResearchConceptsDecision support systemWhole slide imagesTrain accurate classification modelsManually annotated datasetDevelopment of decision support systemsSlide imagesPixel-wise manual annotationSupervised deep learningSupport systemAccurate classification modelDeep learning systemComputer decision support systemDeep learningManual annotationData curationClassification modelLearning systemComputational pathologyDatasetDeploymentMetastasis to axillary lymph nodesAxillary lymph nodesBasal cell carcinomaClinical practiceImages
2018
Prediction of RNA-protein interactions with distributed feature representations and a hybrid deep model
Zhang K, Xiao Y, Pan X, Yang Y. Prediction of RNA-protein interactions with distributed feature representations and a hybrid deep model. 2018, 1-5. DOI: 10.1145/3240876.3240912.Peer-Reviewed Original ResearchPrediction of RNA-protein interactionsRNA sequencingProtein-RNA interactionsRNA-protein interactionsComputational prediction toolsRNA-binding-proteinBiological processesOne-hot vectorDeep learning architectureHybrid deep modelBiological experimentsRNAMachine learning modelsSequenceBenchmark datasetsDeep modelsLearning architectureDistributed representationClassification modelComputational toolsStatistical featuresLearning modelsProteinPrediction toolsPredictive performance2-Channel Convolutional 3D Deep Neural Network (2CC3D) for FMRI Analysis: ASD Classification and Feature Learning
Li X, Dvornek NC, Papademetris X, Zhuang J, Staib LH, Ventola P, Duncan JS. 2-Channel Convolutional 3D Deep Neural Network (2CC3D) for FMRI Analysis: ASD Classification and Feature Learning. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2018, 2018: 1252-1255. PMID: 32983370, PMCID: PMC7519578, DOI: 10.1109/isbi.2018.8363798.Peer-Reviewed Original ResearchConvolutional neural networkNeural networkCNN convolutional layerSpatial featuresASD classificationDeep neural networksMean F-scoreTraditional machineFeature learningConvolutional layersInput formatF-scoreClassification modelTemporal informationNetworkWindow parametersImagesClassificationConvolutionalTemporal statisticsMachineLearningFeaturesFormatScheme
2005
Small, fuzzy and interpretable gene expression based classifiers
Vinterbo S, Kim E, Ohno-Machado L. Small, fuzzy and interpretable gene expression based classifiers. Bioinformatics 2005, 21: 1964-1970. PMID: 15661797, DOI: 10.1093/bioinformatics/bti287.Peer-Reviewed Original ResearchConceptsRule-based classifierBiomedical domainClassification modelFuzzy logicClassifierBiomedical researchersGene expression dataDataset
2002
Logistic regression and artificial neural network classification models: a methodology review
Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review. Journal Of Biomedical Informatics 2002, 35: 352-359. PMID: 12968784, DOI: 10.1016/s1532-0464(03)00034-0.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsMedical data classification tasksNeural network classification modelArtificial neural network (ANN) classification modelData classification tasksNetwork classification modelArtificial neural networkArtificial neural network modelNeural network modelClassification taskNeural networkClassification modelNetwork modelTechnical pointMachineAlgorithmNetworkTaskQuality criteriaModelMethodology reviewSample of papers
1998
Diagnosing breast cancer from FNAs: variable relevance in neural network and logistic regression models.
Ohno-Machado L, Bialek D. Diagnosing breast cancer from FNAs: variable relevance in neural network and logistic regression models. 1998, 52 Pt 1: 537-40. PMID: 10384515.Peer-Reviewed Original Research
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