Fiorillo A. Is treatment resistant depression a different subtype of depression? [J]. Eur Psychiatry, 2021, 64(S1): S41-S41.
[2]
Huang Y, Wang Y, Wang H, et al. Prevalence of mental disorders in China: a cross-sectional epidemiological study [J]. Lancet Psychiatry, 2019, 6(3): 211-224.
[3]
Hawes M T, Szenczy A K, Klein D N, et al. Increases in depression and anxiety symptoms in adolescents and young adults during the COVID-19 pandemic [J]. Psychol Med, 2022, 52(14): 3222-3230.
Sullivan P F, Neale M C, Kendler K S. Genetic epidemiology of major depression: review and meta-analysis [J]. Am J Psychiatry, 2000, 157(10): 1552-1562.
Tolentino J C, Schmidt S L. DSM-5 criteria and depression severity: implications for clinical practice [J]. Front Psychiatry, 2018, 9: 450.
[8]
Grover S, Adarsh H. A comparative study of prevalence of mixed features in patients with unipolar and bipolar depression [J]. Asian J Psychiatr, 2023, 81: 103439.
[9]
Mitchell A J, Vaze A, Rao S. Clinical diagnosis of depression in primary care: a meta-analysis [J]. Lancet, 2009, 374(9690): 609-619.
Kroenke K. When and how to treat subthreshold depression [J]. JAMA, 2017,317(7): 702- 704.
[12]
Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges [J]. Biol Psychiatry Cogn Neurosci Neuroimaging, 2018, 3(3): 223-230.
[13]
Dwyer D B, Falkai P, Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry [J]. Annu Rev Clin Psycho, 2018, 14: 91-118.
[14]
Alhagry S, Aly A, Reda A. Emotion recognition based on EEG using LSTM recurrent neural network [J]. Int J Adv Comput Sci Appl, 2017, 8(10).
[15]
Mumtaz W, Xia L, Ali S S A, et al. Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD) [J]. Biomed Signal Process Control, 2017, 31: 108-115.
[16]
Cai H, Qu Z, Li Z, et al. Feature-level fusion approaches based on multimodal EEG data for depression recognition [J]. Inf Fusion, 2020, 59: 127-138.
[17]
Ye-Ting S, Tao-Lin C, Du H, et al. Research progress of biological markers for depression based on psychoradiology and artificial intelligence [J]. Prog Biochem Biophys, 2019, 46(9): 879-899.
[18]
Dai L, Zhou H, Xu X, et al. Brain structural and functional changes in patients with major depressive disorder: a literature review [J]. Peer J, 2019, 7: e8170.
[19]
Hong S, Liu Y S, Cao B, et al. Identification of suicidality in adolescent major depressive disorder patients using sMRI: a machine learning approach [J]. J Affect Disord, 2021, 280: 72-76.
[20]
Ramasubbu R, Brown E C, Marcil L D, et al. Automatic classification of major depression disorder using arterial spin labeling MRI perfusion measurements [J]. Psychiatry Clin Neurosci, 2019, 73(8): 486-493.
[21]
Kambeitz J, Cabral C, Sacchet M D, et al. Detecting neuroimaging biomarkers for depression: a meta-analysis of multivariate pattern recognition studies [J]. Biol Psychiat, 2017, 82(5): 330-338.
[22]
Huang L, Wei W, Liu Z, et al. Effective schizophrenia recognition using discriminative eye movement features and model-metric based features [J]. Pattern Recognit Lett, 2020, 138: 608-616.
[23]
Alghowinem S, Goecke R, Wagner M, et al. Eye movement analysis for depression detection[C]// 2013 IEEE International Conference on Image Processing. New York: IEEE, 2013: 4220-4224.
[24]
De Silva S, Dayarathna S, Ariyarathne G, et al. A rule-based system for ADHD identification using eye movement data[C]// 2019 Moratuwa Engineering Research Conference (MERCon). New York: IEEE, 2019: 538-543.
[25]
Dan Z A, Xu L A, Lx A, et al. Effective differentiation between depressed patients and controls using discriminative eye movement features [J]. J Affect Disord, 2022, 307: 237-243.
[26]
Zivanovic O, Nedic A. Kraepelin's concept of manic-depressive insanity: one hundred years later [J]. J Affect Disord, 2012, 137(1-3): 15-24.
[27]
He L, Cao C. Automated depression analysis using convolutional neural networks from speech [J]. J Biomed Inform, 2018, 83: 103-111.
[28]
Shin D, Cho W I, Park C H K, et al. Detection of minor and major depression through voice as a biomarker using machine learning [J]. J Clin Med, 2021, 10(14): 3046.
[29]
Schultebraucks K, Yadav V, Shalev A Y, et al. Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood [J]. Psychol Med, 2022, 52(5): 957-967.
Voosen P. The AI detectives [J]. Science, 2017, 357(6346): 22-27.
[32]
Kar K, Kornblith S, Fedorenko E. Interpretability of artificial neural network models in artificial intelligence versus neuroscience[J]. Nat Mach Intell, 2022, 4 (12): 1065-1067.
[33]
Vieira S, Gong Q-y, Pinaya W H, et al. Using machine learning and structural neuroimaging to detect first episode psychosis: reconsidering the evidence [J]. Schizophr Bull, 2020, 46(1): 17-26.
[34]
Lee Y, Ragguett R-M, Mansur R B, et al. Applications of machine learning algorithms to predict therapeutic outcomes in depression: a meta-analysis and systematic review [J]. J Affect disord, 2018, 241: 519-532.
[35]
Ay B, Yildirim O, Talo M, et al. Automated depression detection using deep representation and sequence learning with EEG signals [J]. J Med Syst, 2019, 43(7): 1-12.
[36]
Richer R, Zhao N, Amores J, et al. Real-time mental state recognition using a wearable EEG[C]// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). New York: IEEE, 2018: 5495-5498.
[37]
Tazawa Y, Liang K-c, Yoshimura M, et al. Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning [J]. Heliyon, 2020, 6(2): e03274.
[38]
Kumar S, Yadava M, Roy P P. Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction [J]. Inf Fusion, 2019, 52: 41-52.
2023, Vol. 34 
