[1] 戴晓阳, 蔡太生. 临床心理评估的过去、现在与未来[J]. 中国临床心理学杂志, 2001, 9(3): 237-240.
[2] 张鹏. AI技术在高校学生心理评估中的应用[J]. 中国教育信息化, 2020, 26(10): 90-93.
[3] Luxton D D. An introduction to artificial intelligence in behavioral and mental health care[M]//Artificial Intelligence in Behavioral and Mental Health Care. Amsterdam: Elsevier, 2016: 1-26.
[4] Corcoran C M, Carrillo F, Fernández-Slezak D, et al. Prediction of psychosis across protocols and risk cohorts using automated language analysis[J]. World Psychiatry, 2018, 17(1): 67-75.
[5] Jaiswal S, Valstar M F, Gillott A, et al. Automatic detection of ADHD and ASD from expressive behaviour in RGBD data[C]//Proceedings of 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). Piscataway, NJ: IEEE, 2017: 1-17.
[6] Kalmady S V, Greiner R, Agrawal R, et al. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemblelearning[J]. NPJ Schizophrenia, 2019, 5(1): 2.
[7] Ophir Y, Tikochinski R, Asterhan C S C, et al. Deep neural networks detect suicide risk from textual facebook posts[J]. Scientific Reports, 2020, 10(1): 16685.
[8] Mundt J C, Vogel A P, Feltner D E, et al. Vocal acoustic biomarkers of depression severity and treatment response [J]. Biological Psychiatry, 2012, 72(7): 580-587.
[9] Williamson J R, Young D, Nierenberg A A, et al. Tracking depression severity from audio and video based on speech articulatory coordination[J]. Computer Speech & Language, 2019, 55: 40-56.
[10] Zhu Y, Shang Y Y, Shao Z H, et al. Automated depression diagnosis based on deep networks to encode facial appearance and dynamics[J]. IEEE Transactions on Affective Computing, 2018, 9(4): 578-584.
[11] 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]. Psychological Medicine, 2022, 52(5): 957-967.
[12] Jollans L, Whelan R. Neuromarkers for mental disorders: Harnessing population neuroscience[J]. Frontiers in Psychiatry, 2018, 9: 242.
[13] Luijten M, Schellekens A F, Kühn S, et al. Disruption of reward processing in addiction: An image-based metaanalysis of functional magnetic resonance imaging studies[J]. JAMA Psychiatry, 2017, 74(4): 387-398.
[14] Crossley N A, Mechelli A, Ginestet C, et al. Altered hub functioning and compensatory activations in the connectome: A meta-analysis of functional neuroimaging studies in schizophrenia[J]. Schizophrenia Bulletin, 2016, 42(2): 434-442.
[15] Brühl A B, Delsignore A, Komossa K, et al. Neuroimaging in social anxiety disorder: A meta-analytic review resulting in a new neurofunctional model[J]. Neuroscience and Biobehavioral Reviews, 2014, 47: 260-280.
[16] Plichta M M, Scheres A. Ventral – striatal responsiveness during reward anticipation in ADHD and its relation to trait impulsivity in the healthy population: A meta-analytic review of the fMRI literature[J]. Neuroscience & Biobehavioral Reviews, 2014, 38: 125-134.
[17] Bologna M, Merola A, Ricciardi L. Editorial: Innovative technologies and clinical applications for invasive and non-invasive neuromodulation: From the workbench to the bedside[J]. Frontiers in Neurology, 2019, 10: 1350.
[18] Polanía R, Nitsche M A, Ruff C C. Studying and modifying brain function with non-invasive brain stimulation [J]. Nature Neuroscience, 2018, 21(2): 174-187.
[19] Lysenko S. Influence of speed information transfer on safety of society[J]. International Journal of Recent Technology and Engineering, 2019, 8(4S): 103-109.
[20] 朱孟垚, 李兴华. ChatGPT安全威胁研究[J]. 信息安全研究, 2023, 9(6): 533-542.
[21] 李伟, 孔祥瑜. 人类文明新形态视域下人工智能发展的伦理底线与进路[J]. 宁夏社会科学, 2023(2): 33-39.
[22] 2016第九届数学中国数学建模网络挑战赛[EB/OL]. (2016-05-24)[2024-03-01]. http://www.tzm cm.cn.
[23] 王璐, 朱家明, 刘佩麟, 等. 基于Logistic回归青少年心理的风险评估及预警研究[J]. 齐齐哈尔大学学报(自然科学版), 2017, 33(2): 77-83.
[24] 刘嘉. 多元教育评价助力创新人才培养[J]. 人民教育, 2020(21): 22-29.
[25] 胡传鹏, 王非, 过继成思, 等. 心理学研究中的可重复性问题: 从危机到契机[J]. 心理科学进展, 2016, 24(9): 1504-1518.
[26] 吴凡, 顾全, 施壮华, 等. 跳出传统假设检验方法的陷阱: 贝叶斯因子在心理学研究领域的应用[J]. 应用心理学, 2018, 24(3): 195-202.
[27] 周吉帆, 徐昊骙, 唐宁, 等.“强认知”的心理学研究: 来自AlphaGo的启示[J]. 应用心理学, 2016, 22(1): 3- 11.
[28] 余嘉元, 田金亭, 朱强忠. 计算智能在心理学中的应用[J]. 山东大学学报(工学版), 2009, 39(1): 1-5.
[29] 王一溢, 占继尔, 陈泽龙, 等. 机器学习在心理测量中的应用[J]. 电脑知识与技术, 2021, 17(3): 204-206.
[30] 田玮, 朱廷劭. 基于深度学习的微博用户自杀风险预测[J]. 中国科学院大学学报, 2018, 35(1): 131-136.
[31] 赵靓. 基于微博和网购行为的用户心理压力感知[D]. 北京: 清华大学, 2016.
[32] Zhao N, Zhang Z, Wang Y M, et al. See your mental state from your walk: Recognizing anxiety and depression through kinect-recorded gait data[J]. PLoS One, 2019, 14(5): e0216591.
[33] Gao Z K, Li Y L, Yang Y X, et al. A GPSO-optimized convolutional neural networks for EEG-based emotion recognition[J]. Neurocomputing, 2020, 380: 225-235.
[34] 尚铭悦. 数据驱动的心理测评量表优化方法研究与应用[D]. 济南: 济南大学, 2022.
[35] 李刚, 陈洁. 基于数据驱动的高校学生心理健康评估研究[J]. 微型电脑应用, 2022, 38(11): 163-166.
[36] 卢艺, 崔中良. 中国人工智能伦理研究进展[J]. 科技导报, 2022, 40(18): 69-78.
[37] 国务院关于印发新一代人工智能发展规划的通知[J]. 中华人民共和国国务院公报, 2017(22): 7-21.
[38] 王远旭, 汤一鸣. 医疗人工智能伦理: 问题·原因·对策[J]. 武汉冶金管理干部学院学报, 2023, 33(1): 25-28.
[39] 张钹, 朱军, 苏航. 迈向第三代人工智能[J]. 中国科学(信息科学), 2020, 50(9): 1281-1302.
[40] 任萍, 汪悦, 刘冬予, 等. 心理健康评估与干预的智能化应用[J]. 北京师范大学学报(社会科学版), 2022(4): 150-160.
[41] 王从余, 彭凯平. 智能社会的心理影响与研究展望[J]. 海南大学学报(人文社会科学版), 2024, 42(1): 76-82.
[42] 姚峰. 当代中国家庭问题和家庭复原力分析: 以家庭伦理为考察脉络[J]. 九江学院学报(社会科学版), 2017, 36(4): 76-82.
[43] Millings A, Morris J, Rowe A, et al. Can the effectiveness of an online stress management program be augmented by wearable sensor technology?[J]. Internet Interventions, 2015, 2(3): 330-339.
[44] 时晔. 人工智能在5G通信领域上的发展探究[J]. 电子测试, 2021(8): 131-132.
[45] 申文韬. 5G通信技术与人工智能技术融合发展的基本现状与演化趋势[J]. 计算机产品与流通, 2020(7): 38.
[46] 佟志勇. 人工智能技术在5G网络中的应用[J]. 无线互联科技, 2021, 18(4): 1-2.
[47] 姚峰. 生命历程理论视角下青少年越轨行为成因与防控的质性研究[J]. 中国监狱学刊, 2024, 39(2): 59-66.
[48] 史梦璐. 当事人对“AI”心理咨询的知觉和体验研究[D]. 武汉: 华中师范大学,2018.
[49] Cook J E, Doyle C. Working alliance in online therapy as compared to face-to-face therapy: Preliminary results [J]. Cyberpsychology & Behavior, 2002, 5(2): 95-105.
[50] 宗阳, 王广新. 拟人化: 人机交互中的心理学应用[J]. 心理技术与应用, 2016, 4(5): 296-305.
[51] de Graaf M M A, Ben Allouch S, Klamer T. Sharing a life with Harvey: Exploring the acceptance of and relationship-building with a social robot[J]. Computers in Human Behavior, 2015, 43: 1-14.
[52] Sampson R J, Laub J H. Crime in the making: Pathways and turning points through life[J]. Choice Reviews Online, 1993, 31(3): 430.
[53] Sampson R J, Laub J H. Crime and deviance in the life course[J]. Annual Review of Sociology, 1992, 18: 63-84.
[54] Temel Y, Hescham S A, Jahanshahi A, et al. Neuromodulation in psychiatric disorders[J]. International Review of Neurobiology, 2012, 107: 283-314.
[55] Read J, Bentall R. The effectiveness of electroconvulsive therapy: A literature review[J]. Epidemiology and Psychiatric Sciences, 2010, 19(4): 333-347.
[56] Baghai T C, Möller H J. Electroconvulsive therapy and its different indications[J]. Dialogues in Clinical Neuroscience, 2008, 10(1): 105-117.
[57] Sahlem G L, Short E B, Kerns S, et al. Expanded safety and efficacy data for a new method of performing electroconvulsive therapy: Focal electrically administered seizure therapy[J]. The Journal of ECT, 2016, 32(3): 197- 203.
[58] Magnezi R, Aminov E, Shmuel D, et al. Comparison between neurostimulation techniques repetitive transcranial magnetic stimulation vs electroconvulsive therapy for the treatment of resistant depression: Patient preference and cost-effectiveness[J]. Patient Preference and Adherence, 2016, 10: 1481-1487.
[59] Hallett M. Transcranial magnetic stimulation: A primer [J]. Neuron, 2007, 55(2): 187-199.
[60] Taylor S F, Bhati M T, Dubin M J, et al. A naturalistic, multi-site study of repetitive transcranial magnetic stimulation therapy for depression[J]. Journal of Affective Disorders, 2017, 208: 284-290.
[61] Dunner D L, Aaronson S T, Sackeim H A, et al. A multisite, naturalistic, observational study of transcranial magnetic stimulation for patients with pharmacoresistant major depressive disorder: Durability of benefit over a 1- year follow-up period[J]. The Journal of Clinical Psychiatry, 2014, 75(12): 1394-1401.
[62] 康克. 经颅磁刺激机器人靶点自动定位及跟踪方法研究[D]. 北京: 北京石油化工学院, 2023.
[63] 唐睿, 宋洪文, 孔卓, 等. 经颅直流电刺激治疗常见神经精神疾病的临床应用专家共识[J]. 中华精神科杂志, 2022, 55(5): 327-382.
[64] Tavakoli A V, Yun K. Transcranial alternating current stimulation (tACS) mechanisms and protocols[J]. Frontiers in Cellular Neuroscience, 2017, 11: 214.
[65] 唐代兴. 善抑或恶: 人工智能的根本伦理问题[J]. 人文杂志, 2022(6): 76-87.
[66] 姜力铭, 田雪涛, 任萍, 等. 人工智能辅助下的心理健康新型测评[J]. 心理科学进展, 2022(1): 157-167.
[67] Kern M L, Park G, Eichstaedt J C, et al. Gaining insights from social media language: Methodologies and challenges[J]. Psychological Methods, 2016, 21(4): 507- 525.
[68] Berman J J. Principles of big data: Preparing, sharing, and analyzing complex information[M]. Boston: Morgan Kaufmann, 2013.
[69] Harari G M, Vaid S S, Müller S R, et al. Personality sensing for theory development and assessment in the digital age[J]. European Journal of Personality, 2020, 34(5): 649-669.
[70] 叶丽频, 陈旻. 心理干预研究的伦理问题与应对[J]. 医学与哲学, 2019, 40(10): 28-31.
[71] 王萍萍. 人工智能时代机器人的伦理关怀探析: 以《老子》“善”论为视角[J]. 自然辩证法研究, 2021, 37(5): 54-59.
[72] 许浩, 程卿玄, 董晶, 等. 人智交互情境下的未成年人个人信息保护: 困境与出路[J/OL]. 情报理论与实践, 1-12. [2024-07-20]. http://kns. cnki. net/kcms/detail/11. 1762.G3.20240220.1600.004.html.