2025
Causal Modeling of FMRI Time-Series for Interpretable Autism Spectrum Disorder Classification
Duan P, Dvornek N, Wang J, Staib L, Duncan J. Causal Modeling of FMRI Time-Series for Interpretable Autism Spectrum Disorder Classification. 2025, 00: 1-5. DOI: 10.1109/isbi60581.2025.10980933.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingAutism spectrum disorderState-of-the-art modelsState-of-the-artFMRI time seriesDeep learning classifierDeep learning modelsTime series informationLearning classifiersClassification accuracyNon-linear interactionsMachine learningLeft precuneusRight precuneusABIDE datasetBrain regionsLearning modelsASD populationSpectrum disorderDisorder classificationASD classificationBrain signalsASD biomarkersDevelopmental disordersCorrelation-based models
2024
Non-invasive Electrolyte Estimation Using Multi-lead ECG Data via Semi-Supervised Contrastive Learning with an Adaptive Loss
Nowroozilarki Z, Huang S, Khera R, Mortazavi B. Non-invasive Electrolyte Estimation Using Multi-lead ECG Data via Semi-Supervised Contrastive Learning with an Adaptive Loss. 2024, 00: 1-8. DOI: 10.1109/bhi62660.2024.10913552.Peer-Reviewed Original ResearchState-of-the-art modelsAdaptive lossSemi-supervised contrastive learningTrain machine learning-based modelsState-of-the-artClassification of electrocardiogramElectronic health record datasetLearning-based modelsMachine learning-based modelsContrastive learningLabel scarcityUnlabeled datasetRegression tasksClassification taskECG-dataRecord datasetData pointsLabeling frequencyDatasetTaskDataBackpropagationEncodingAccurate predictionLabelingImproving large language models for clinical named entity recognition via prompt engineering
Hu Y, Chen Q, Du J, Peng X, Keloth V, Zuo X, Zhou Y, Li Z, Jiang X, Lu Z, Roberts K, Xu H. Improving large language models for clinical named entity recognition via prompt engineering. Journal Of The American Medical Informatics Association 2024, 31: 1812-1820. PMID: 38281112, PMCID: PMC11339492, DOI: 10.1093/jamia/ocad259.Peer-Reviewed Original ResearchClinical NER tasksNER taskTask-specific promptsEntity recognitionLanguage modelTraining samplesState-of-the-art modelsFew-shot learningState-of-the-artMinimal training dataTask-specific knowledgeF1-socreAnnotated samplesConcept extractionModel performanceAnnotated datasetsTraining dataF1 scoreTask descriptionFormat specificationsComplex clinical dataOptimal performanceTaskEvaluation schemaGPT model
2023
Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning
Lee D, Son S, Jeon H, Kim S, Han J. Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning. 2023, 4357-4369. DOI: 10.1145/3580305.3599917.Peer-Reviewed Original ResearchBipolar disorderBD patientsPredicting future suicideIncreased risk of suicideRisk of suicideFuture suicideMulti-task learning modelBD symptomsState-of-the-art modelsCurrent symptomsState-of-the-artSuicideSuicide preventionMultitask learningAttention weightsSymptom identificationAttention mechanismPrediction taskDisordersSymptomsLearning modelsPsychiatristsBipolarSocial mediaSuicide 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 ideationPerformanceOutlier Robust Disease Classification via Stochastic Confidence Network
Lee K, Lee H, El Fakhri G, Sepulcre J, Liu X, Xing F, Hwang J, Woo J. Outlier Robust Disease Classification via Stochastic Confidence Network. Lecture Notes In Computer Science 2023, 14394: 80-90. DOI: 10.1007/978-3-031-47425-5_8.Peer-Reviewed Original ResearchDeep learningState-of-the-art modelsAccuracy of deep learningState-of-the-artMedical image dataMedical imaging modalitiesImage patchesIrrelevant patchesCategorical featuresPresence of outliersDL modelsConfidence networkConfidence predictionsClassifying outliersData samplesImage dataOutliersExperimental resultsDisease classificationImprove diagnostic performanceClassificationDiagnosing breast tumorsUltrasound imagingPerformanceImages
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