2025
Acoustic-based machine learning approaches for depression detection in Chinese university students
Wei Y, Qin S, Liu F, Liu R, Zhou Y, Chen Y, Xiong X, Zheng W, Ji G, Meng Y, Wang F, Zhang R. Acoustic-based machine learning approaches for depression detection in Chinese university students. Frontiers In Public Health 2025, 13: 1561332. PMID: 40443925, PMCID: PMC12119278, DOI: 10.3389/fpubh.2025.1561332.Peer-Reviewed Original ResearchConceptsPatient Health Questionnaire-9Mel-frequency cepstral coefficientsLinear discriminant analysisMachine learning algorithmsAcoustic featuresLearning algorithmsIdentification of depressionMonitoring of depressionCross-sectional studyGlobal public health problemSHapley Additive exPlanationsDepression screeningSelf-report methodsPublic health problemIdentifying DepressionLinear discriminant analysis modelDepression assessmentSupport vector classificationAutomated identificationMachine learning approachArea under the curveHealth problemsOpenSMILE toolkitLogistic regressionCepstral coefficients
2014
Gender and Stress in Predicting Depressive Symptoms Following Stroke
Mazure CM, Weinberger AH, Pittman B, Sibon I, Swendsen J. Gender and Stress in Predicting Depressive Symptoms Following Stroke. Cerebrovascular Diseases 2014, 38: 240-246. PMID: 25401293, PMCID: PMC4283501, DOI: 10.1159/000365838.Peer-Reviewed Original ResearchConceptsPost-stroke depressionPost-stroke patientsDepressive symptomsStressful life eventsClinic visitsElectronic momentary assessmentRisk of PSDThree monthsLife eventsStandardized depression scalesRegular clinic visitsSpecific depressive symptomsStandard clinical assessmentPredicting Depressive SymptomsStroke centersAppetite changesProspective studyFunctional outcomeClinical assessmentDepression ScaleDepression assessmentGender differencesAmbulatory monitoringSignificant associationSymptoms
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