Enhanced 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(MAETowards Disease-Aware Self-Supervised Dynamic Brain Network Learning For Mental Diagnosis
Jin Z, Wen G, Cao P, Liu L, Yang J, Zhu X, Zaiane O, Wang F. Towards Disease-Aware Self-Supervised Dynamic Brain Network Learning For Mental Diagnosis. 2024, 00: 2270-2274. DOI: 10.1109/icassp48485.2024.10446417.Peer-Reviewed Original ResearchState-of-the-art methodsRepresentation learning frameworkSupervised learning schemeSelf-attention mechanismState-of-the-artNetwork learning methodReconstruction lossContrastive lossPoor generalizationLearning schemeLearning frameworkGraph structureLearning methodsTopological informationLearning modelsCross-decodingDiagnosis resultsBrain network analysisDynamic brain network analysisMajor depressive disorderAutism spectrum disorderInformationDynamic brain networksBipolar disorderDecoding