多参数人工智能对评估抑郁症的研究进展

李玉石, 陈旭义, 朱达仁, 王振国

武警医学 ›› 2023, Vol. 34 ›› Issue (8) : 730-734.

PDF(987 KB)
PDF(987 KB)
武警医学 ›› 2023, Vol. 34 ›› Issue (8) : 730-734.
综述

多参数人工智能对评估抑郁症的研究进展

  • 李玉石1, 陈旭义2, 朱达仁1 综述, 王振国2 审校
作者信息 +
文章历史 +

引用本文

导出引用
李玉石, 陈旭义, 朱达仁, 王振国. 多参数人工智能对评估抑郁症的研究进展[J]. 武警医学. 2023, 34(8): 730-734
中图分类号: R318.6   

参考文献

[1] 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.
[4] 段力萨, 郭宇明, 孙江男, 等. 新冠肺炎疫情下某部队三甲医院官兵员工心理健康状况调查分析 [J]. 武警医学, 2020, 31(3): 191-194.
[5] 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.
[6] 陶 然, 纪文博, 张惠敏. 青少年抑郁症研究新进展 [J]. 武警医学, 2015, 26(2): 109-112.
[7] 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.
[10] 都业铭, 张云巧, 王宗琦,等. 重性抑郁障碍患者躯体症状与脑源性神经营养因子和炎性因子的相关性研究 [J]. 中国全科医学, 2023, 26(12): 1463.
[11] 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.
[30] 周爱保, 鲁小勇, 吴文意, 等. 采用语音的抑郁症诊断研究述评 [J]. 小型微型计算机系统, 2017, 38(11): 2619-2624.
[31] 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.

PDF(987 KB)

Accesses

Citation

Detail

段落导航
相关文章

/