Exploring the relationship between response time sequence in scale answering process and severity of insomnia: A machine learning approach
Su Z, Liu R, Zhou K, Wei X, Wang N, Lin Z, Xie Y, Wang J, Wang F, Zhang S, Zhang X. Exploring the relationship between response time sequence in scale answering process and severity of insomnia: A machine learning approach. Heliyon 2024, 10: e33485. PMID: 39040408, PMCID: PMC11261114, DOI: 10.1016/j.heliyon.2024.e33485.Peer-Reviewed Original ResearchResponse time dataInsomnia Severity IndexInsomnia symptomsPsychological measuresPresence of insomnia symptomsIndividual question levelSeverity of insomniaSymptom severityPsychological evaluationResponse timeInsomniaSleep qualityMachine learning modelsSeverity IndexSymptomsQuestion levelTotal response timeParticipantsLearning modelsTime dataPotential utilityEvaluate sleep qualitySeverityMachine learning approachMobile applicationsTowards 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