1北京师范大学心理学部, 北京 100875;
2北京交通大学计算机与信息技术学院, 北京 100044;
3中国基础教育质量监测协同创新中心, 北京 100875
收稿日期:2021-02-22出版日期:2022-01-15发布日期:2021-11-25通讯作者:骆方, E-mail: luof@bnu.edu.cn基金资助:* 国家自然科学基金联合基金项目(U1911201)A new type of mental health assessment using artificial intelligence technique
JIANG Liming1, TIAN Xuetao2, REN Ping3, LUO Fang11School of Psychology, Beijing Normal University, Beijing 100875, China;
2School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;
3Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, Beijing 100875, China
Received:2021-02-22Online:2022-01-15Published:2021-11-25摘要/Abstract
摘要： 近年来, 人工智能技术的飞速发展及应用催生了“智能化心理健康测评”这一领域。智能化心理健康测评能够弥补传统方法的不足, 降低漏诊率并提高诊断效率, 这对于心理健康问题的普查及预警具有重大意义。目前, 智能化心理健康测评处于初步发展阶段, 研究者基于在线行为数据、便携式设备数据等开展主要以数据驱动为导向的探索研究, 旨在实现更高的预测准确率, 但是测评结果的可解释性等指标尚不够理想。未来的智能化心理健康测评需要强调心理学领域知识和经验的深度介入, 提高测评的针对性和精细化程度, 加强信效度检验, 这对于智能化心理健康测评工具的进一步发展和应用至关重要。
参考文献
| [1] 骆方, 姜力铭, 田雪涛, 肖梦格, 马彦珍, 张生. (2021). 小学生羞怯特质预测及语言风格模型构建. [2] 孙鑫, 黎坚, 符植煜. (2018). 利用游戏log-file预测学生推理能力和数学成绩——机器学习的应用. [3] 汪静莹, 朱廷劭, 郝碧波, 刘天俐. (2016). 微博用户生活满意度微博语言及行为特征分析. [4] 徐俊怡, 李中权. (2021). 基于游戏的心理测评. [5] 张晗, 贾甜远, 骆方, 张生, 邬霞. (2020). 面向网络文本的 BERT 心理特质预测研究. [6] Afshan A., Guo J. X., Park S. J., Ravi V., Flint A., & Alwan A. (2018). Effectiveness of voice quality features in detecting depression.Interspeech, 1676-1680. [7] Alhagry S., Fahmy A. A., & El-Khoribi R. A. (2017). Emotion recognition based on EEG using LSTM recurrent neural network. [8] Aung, Z. M. M., & Myint, P. H. (2019 July). Personality prediction based on content of facebook users: A literature review. [9] Ay B., Yildirim O., Talo M., Baloglu U. B., Aydin G., Puthankattil S. D., & Acharya U. R. (2019). Automated depression detection using deep representation and sequence learning with EEG signals. [10] Berman, J. J. (2013). [11] Bosl W. J., Tager-Flusberg H., & Nelson C. A. (2018). EEG analytics for early detection of autism spectrum disorder: A data-driven approach. [12] Brihadiswaran G., Haputhanthri D., Gunathilaka S., Meedeniya D., & Jayarathna S. (2019). EEG-based processing and classification methodologies for autism spectrum disorder: A review. [13] Bzdok, D., & Meyer-Lindenberg, A. (2018). Machine learning for precision psychiatry: Opportunities and challenges. [14] Cannizzaro M., Harel B., Reilly N., Chappell P., & Snyder P. J. (2004). Voice acoustical measurement of the severity of major depression. [15] Cardone, D., & Merla, A. (2017). New frontiers for applications of thermal infrared imaging devices: Computational psychopshysiology in the neurosciences. [16] Castaldo R., Montesinos L., Melillo P., James C., & Pecchia L. (2019). Ultra-short term HRV features as surrogates of short term HRV: A case study on mental stress detection in real life. [17] Chen, E. E., & Wojcik, S. P. (2016). A practical guide to big data research in psychology. [18] Chen M., Cao Y., Wang R., Li Y., Wu D., & Liu Z. C. (2019). DeepFocus: Deep encoding brainwaves and emotions with multi-scenario behavior analytics for human attention enhancement. [19] Cheng Q. J., Li T. M., Kwok C. L., Zhu T. S., & Yip P. S. (2017). Assessing suicide risk and emotional distress in Chinese social media: A text mining and machine learning study. [20] Cipresso P., Colombo D., & Riva G. (2019). Computational psychometrics using psychophysiological measures for the assessment of acute mental stress. [21] de Melo W. C., Granger E., & Lopez M. B. (2020, May). Encoding temporal information for automatic depression recognition from facial analysis. [22] Deng Y. L., Wu F., Du L., Zhou R. L., & Cao L. H. (2019, March). EEG-based identification of latent emotional disorder using the machine learning approach. [23] DeRosier, M. E., & Thomas, J. M. (2018). Establishing the criterion validity of Zoo U's game-based social emotional skills assessment for school-based outcomes. [24] de Silva S., Dayarathna S., Ariyarathne G., Meedeniya D., Jayarathna S., Michalek A. M. P., & Jayawardena G. (2019, July). A rule-based system for ADHD identification using eye movement data. [25] DiCerbo, K. E. (2014). Game-based assessment of persistence. [26] Dubreuil-Vall L., Ruffini G., & Camprodon J. A. (2020). Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG.Frontiers in Neuroscience, 14, 251. [27] Eichstaedt J. C., Smith R. J., Merchant R. M., Ungar L. H., Crutchley P., Preoţiuc-Pietro D., .. Schwartz H. A. (2018). Facebook language predicts depression in medical records. [28] Flynn R. M., Colo?n-Acosta N., Zhou J., & Bower J. (2019). A game-based repeated assessment for cognitive monitoring: Initial usability and adherence study in a summer camp setting. [29] Guo Z. H., Wu X., Liu J. H., Yao L., & Hu B. (2018). Altered electroencephalography functional connectivity in depression during the emotional face-word Stroop task. [30] Haque A., Guo M., Miner A. S., & Fei-Fei L. (2018). Measuring depression symptom severity from spoken language and 3D facial expressions. [31] Harari G. M., Vaid S. S., Müller S. R., Stachl C., Marrero Z., Schoedel R., Bühner M., & Gosling S. D. (2020). Personality sensing for theory development and assessment in the digital age. [32] Hautala J., Heikkila? R., Nieminen L., Rantanen V., Latvala J. M., & Richardson U. (2020). Identification of reading difficulties by a digital game-based assessment technology. [33] He Q. W., Veldkamp B. P., & de Vries T. (2012). Screening for posttraumatic stress disorder using verbal features in self narratives: A text mining approach. [34] He Q. W., Veldkamp B. P., Glas C. A. W., & de Vries T. (2017). Automated assessment of patients' self-narratives for posttraumatic stress disorder screening using natural language processing and text mining. [35] Heinzen T. E., Landrum R. E., Gurung R. A. R., & Dunn D. S. (2015). Game-based assessment: The mash-up we've been waiting for. Gamification in Education and Business(pp. 201-217). Springer. [36] Ihmig F. R., Gogeascoechea H A., Neurohr-Parakenings F., Schäfer S. K., Lass-Hennemann J., & Michael T. (2020). On-line anxiety level detection from biosignals: Machine learning based on a randomized controlled trial with spider-fearful individuals. [37] Ive J., Gkotsis G., Dutta R., Stewart R., & Velupillai S. (2018, June). Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health.Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic(pp. 69-77). [38] Jiang X. L., Chen Y. Q., Huang W. L., Zhang T., Gao C. L., Xing Y. B., & Zheng Y. (2020, April). WeDA: Designing and evaluating a scale-driven wearable diagnostic assessment system for children with ADHD.Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems(pp. 1-12). [39] Johannes Dechant M., Frommel J., & Mandryk R. (2021, May). Assessing social anxiety through digital biomarkers embedded in a gaming task.Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems(pp. 1-15). [40] Kern M. L., Park G., Eichstaedt J. C., Schwartz H. A., Sap M., Smith L. K., & Ungar L. H. (2016). Gaining insights from social media language: Methodologies and challenges. [41] Kodratoff, Y. (2014). [42] Kosinski M., Wang Y., Lakkaraju H., & Leskovec J. (2016). Mining big data to extract patterns and predict real-life outcomes. [43] Latynov, V., & Shepeleva, E. (2020). Applied aspects of the use of algorithms of digital psychometrics. [44] Laxminarayan S., Wang C., Oyama T., Cashmere J. D., Germain A., & Reifman J. (2020). Identification of veterans with PTSD based on EEG features collected during sleep. [45] Lecun Y., Bengio Y., & Hinton G. (2015). Deep learning. [46] Lee, J. E., & Recker, M. (2017). Measuring students' use of self-regulated learning strategies from learning management system data: An evidence-centered design approach.Analytics for Learning, 1-14. [47] Lo J. C., Sehic E., & Meijer S. A. (2017). Measuring mental workload with low-cost and wearable sensors: Insights into the accuracy, obtrusiveness, and research usability of three instruments. [48] Lynn V., Balasubramanian N., & Schwartz H. A. (2020, July). Hierarchical modeling for user personality prediction: The role of message-level attention.Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics(pp. 5306-5316). [49] Mandryk, R. L., & Birk, M. V. (2019). The potential of game-based digital biomarkers for modeling mental health. [50] Mandryk R. L., Dielschneider S., Kalyn M. R., Bertram C. P., Gaetz M., Doucette A., .. Keiver K. (2013, June). Games as neurofeedback training for children with FASD.Proceedings of the 12th International Conference on Interaction Design and Children(pp. 165-172). [51] Manera V., Petit P. D., Derreumaux A., Orvieto I., Romagnoli M., Lyttle G., .. Robert P. H. (2015). “Kitchen and cooking”, a serious game for mild cognitive impairment and Alzheimer's disease: A pilot study. [52] Marouf A. A., Hasan M. K., Mahmud H. (2019 February). Identifying neuroticism from user generated content of social media based on psycholinguistic cues.2019 International Conference on Electrical, Computer and Communication Engineering(pp. 1-5). [53] Meyer T., Quaedflieg C. W. E. M., Weijland K., Schruers K., Merckelbach H., & Smeets T. (2018). Frontal EEG asymmetry during symptom provocation predicts subjective responses to intrusions in survivors with and without PTSD. [54] Mislevy, R. J. (2013). Evidence-centered design for simulation-based assessment. [55] Mislevy R. J., Almond R. G., & Lukas J. F. (2003). A brief introduction to evidence-centered design. [56] Mislevy, R. J., & Haertel, G. D. (2006). Implications of evidence‐centered design for educational testing. [57] Mundt J. C., Vogel A. P., Feltner D. E., & Lenderking W. R. (2012). Vocal acoustic biomarkers of depression severity and treatment response. [58] Pan Z. Y., Ma H. M., Zhang L., & Wang Y. H. (2019). Depression detection based on reaction time and eye movement. [59] Park G., Schwartz H. A., Eichstaedt J. C., Kern M. L., Kosinski M., Stillwell D. J., .. Seligman, M. E. P. (2015). Automatic personality assessment through social media language. [60] Pereira T., Almeida P. R., Cunha J. P., & Aguiar A. (2017). Heart rate variability metrics for fine-grained stress level assessment. [61] Pluntke U., Gerke S., Sridhar A., Weiss J., & Michel B. (2019, July). Evaluation and classification of physical and psychological stress in firefighters using heart rate variability. [62] Polyak S. T., von Davier A. A., & Peterschmidt K. (2017). Computational psychometrics for the measurement of collaborative problem solving skills. [63] Rahwan I., Cebrian M., Obradovich N., Bongard J., Bonnefon J. F., Breazeal C., .. Wellman M. (2019). Machine behaviour. [64] Richer R., Zhao N., Amores J., Eskofier B. M., & Paradiso J. A. (2018, July). Real-time mental state recognition using a wearable EEG. [65] Saef R., Woo S. E., Carpenter J., & Tay L. (2018). Fostering socio-informational behaviors online: The interactive effect of openness to experience and extraversion. [66] Sajjadiani S., Sojourner A. J., Kammeyer-Mueller J. D., & Mykerezi E. (2019). Using machine learning to translate applicant work history into predictors of performance and turnover. [67] Shute, V. J. (2011). Stealth assessment in computer-based games to support learning. [68] Shute V. J., Wang L. B., Greiff S., Zhao, W. N, & Moore, G. (2016). Measuring problem solving skills via stealth assessment in an engaging video game. [69] Snow E., Rutstein D., Basu S., Bienkowski M., & Everson H. T. (2019). Leveraging evidence-centered design to develop assessments of computational thinking practices. [70] Song H., Yi D. J., & Park H. J. (2020). Validation of a mobile game-based assessment of cognitive control among children and adolescents. [71] Song T. F., Zheng W. M., Song P., & Cui Z. (2018). EEG emotion recognition using dynamical graph convolutional neural networks. [72] Song, Y., & Sparks, J. R. (2019). Measuring argumentation skills through a game-enhanced scenario-based assessment. [73] Stachl C., Au Q., Schoedel R., Gosling S. D., Harari G. M., Buschek D., .. Bühner M. (2020). Predicting personality from patterns of behavior collected with smartphones. [74] Tay L., Woo S. E., Hickman L., & Saef R. M. (2020). Psychometric and validity issues in machine learning approaches to personality assessment: A focus on social media text mining. [75] van Nimwegen C., van Oostendorp H., Modderman J., & Bas M. (2011). A test case for GameDNA: Conceptualizing a serious game to measure personality traits. 16th International Conference on Computer Games (CGAMES)(pp. 217-222). IEEE. [76] Ventura, M., & Shute, V. (2013). The validity of a game-based assessment of persistence. [77] Vogel, L. (2018). AI opens new frontier for suicide prevention. [78] von Davier A. A., Deonovic B., Yudelson M., Polyak S. T., & Woo A. (2019, July). Computational psychometrics approach to holistic learning and assessment systems. [79] von Davier, A. A., & Halpin, P. F. (2013). Collaborative problem solving and the assessment of cognitive skills: Psychometric considerations. [80] Voosen, P. (2017). The AI detectives. [81] Wang J. Y., Zhang L., Liu T. L., Pan W., Hu B., & Zhu T. S. (2019). Acoustic differences between healthy and depressed people: A cross-situation study. [82] Wang Q. X., Yang H. X., & Yu Y. H. (2018). Facial expression video analysis for depression detection in Chinese patients. [83] Wen W. H., Liu G. Y., Mao Z. H., Huang W. J., Zhang X., Hu H., .. Jia W. Y. (2018). Toward constructing a real-time social anxiety evaluation system: Exploring effective heart rate features. [84] Williamson J. R., Godoy E., Cha M., Schwarzentruber A., Khorrami P., Gwon Y., .. Quatieri T. F. (2016, October). Detecting depression using vocal, facial and semantic communication cues.Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge(pp. 11-18). [85] Yu H. B., Li F., Wu T., Li R., Yao L., Wang C. Y., & Wu X. (2018). Functional brain abnormalities in major depressive disorder using the Hilbert-Huang transform. [86] Zhang X. W., Pan J., Shen J., Din Z. U., Li J. L., Lu D. W., .. Hu B. (2020). Fusing of electroencephalogram and eye movement with group sparse canonical correlation analysis for anxiety detection. [87] Zhao N., Zhang Z., Wang Y. M., Wang J. Y., Li B. B., Zhu T. S., & Xiang Y. Y. (2019). See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data. [88] Zhu Z. Y., Zhen Z. L., Wu X., & Li S. (2020). |
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