机器学习在心理健康中的运用
Application of Machine Learning in Mental Health
DOI: 10.12677/ASS.2022.1111656, PDF,   
作者: 郑本汇源:福建师范大学心理学院,福建 福州
关键词: 大数据机器学习心理健康Big Data Machine Learning Mental Health
摘要: 在社会快速发展以及疫情流行的大背景下,我国民众的心理健康问题日益严重,引进新的心理监督预测技术刻不容缓。机器学习作为人工智能主要的子领域之一,可以自动从数据中学习模型以做出更好的决策。由于其计算以及预测结果相较于人工更迅速准确,今年已被多国引入心理健康领域开始运用,并在精神疾病诊断、治疗和支持、研究和临床管理等一系列领域展现其作用。本文基于现状,介绍了当前机器学习在心理健康领域的运用,以及在该领域进一步发展的期望。
Abstract: In the context of rapid social development as well as epidemic epidemics, the mental health problems of our population are becoming increasingly serious and the introduction of new psychological supervision and prediction techniques is urgent. Machine learning, one of the main subfields of artificial intelligence, can automatically learn models from data to make better decisions. Because its computation, as well as prediction results, are faster and more accurate compared to human, it has been introduced into the mental health field and started to be used in several countries this year, and has demonstrated its usefulness in a range of areas such as mental illness diagnosis, treatment and support, research and clinical management. This paper presents the current use of machine learning in mental health based on the current state of affairs and the expectations for further development in this field.
文章引用:郑本汇源. 机器学习在心理健康中的运用[J]. 社会科学前沿, 2022, 11(11): 4814-4848. https://doi.org/10.12677/ASS.2022.1111656

参考文献

[1] Huang, Y., Wang, Y.U., Wang, H., et al. (2019) Prevalence of Mental Disorders in China: A Cross-Sectional Epidemiological Study. The Lancet Psychiatry, 6, 211-224. [Google Scholar] [CrossRef
[2] Kosinski, M., Stillwell, D. and Graepel, T. (2013) Private Traits and Attributes Are Predictable from Digital Records of Human Behavior. Proceedings of the National Academy of Sciences, 110, 5802-5805. [Google Scholar] [CrossRef] [PubMed]
[3] 苏悦, 刘明明, 赵楠, 刘晓倩, 朱廷劭. 基于社交媒体数据的心理指标识别建模: 机器学习的方法[J]. 心理科学进展, 2021, 29(4): 571-585.
[4] 朱廷劭, 汪静莹, 赵楠, 刘晓倩. 论大数据时代的心理学研究变革[J]. 新疆师范大学学报(哲学社会科学版), 2015, 36(4): 100-107+2.
[5] Jordan, M.I. and Mitchell, T.M. (2015) Machine Learning: Trends, Perspectives, and Prospects. Science, 349, 255-260. [Google Scholar] [CrossRef] [PubMed]
[6] Oseguera, O., Rinaldi, A., Tuazon, J., et al. (2017) Automatic Quantification of the Veracity of Suicidal Ideation in Counseling Transcripts. In: Stephanidis, C., Ed., International Conference on Human-Computer Interaction, Springer, Cham, 473-479. [Google Scholar] [CrossRef
[7] Strous, R.D., Koppel, M., Fine, J., et al. (2009) Automated Characterization and Identification of Schizophrenia in Writing. The Journal of Nervous and Mental Disease, 197, 585-588. [Google Scholar] [CrossRef
[8] Wu, J.L., Yu, L.C. and Chang, P.C. (2012) Detecting Causality from Online Psychiatric Texts Using Inter-Sentential Language Patterns. BMC Medical Informatics and Decision Making, 12, Article No. 72. [Google Scholar] [CrossRef] [PubMed]
[9] Sano, A., Phillips, A.J., Amy, Z.Y., et al. (2015) Recognizing Academic Performance, Sleep Quality, Stress Level, and Mental Health Using Personality Traits, Wearable Sensors and Mobile Phones. 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, 9-12 June 2015, 1-6. [Google Scholar] [CrossRef
[10] Alam, M.G.R., Abedin, S.F., Al Ameen, M., et al. (2016) Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction. Sensors, 16, Article No. 1431. [Google Scholar] [CrossRef] [PubMed]
[11] Kliper, R., Portuguese, S. and Weinshall, D. (2015) Prosodic Analysis of Speech and the Underlying Mental State. In: Serino, S., et al., Eds., International Symposium on Pervasive Computing Paradigms for Mental Health, Springer, Cham, 52-62. [Google Scholar] [CrossRef
[12] Fraser, K.C., Meltzer, J.A. and Rudzicz, F. (2016) Linguistic Features Identify Alzheimer’s Disease in Narrative Speech. Journal of Alzheimer’s Disease, 49, 407-422. [Google Scholar] [CrossRef
[13] Sheela Kumari, R., Varghese, T., Kesavadas, C., et al. (2014) Longitudinal Evaluation of Structural Changes in Frontotemporal Dementia Using Artificial Neural Networks. In: Satapathy, S.C., et al., Eds., Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), Springer, Cham, 165-172. [Google Scholar] [CrossRef
[14] Doan, N.T., Engvig, A., Zaske, K., et al. (2017) Distinguishing Early and Late Brain Aging from the Alzheimer’s Disease Spectrum: Consistent Morphological Patterns across Independent Samples. Neuroimage, 158, 282-295. [Google Scholar] [CrossRef] [PubMed]
[15] Bosl, W.J., Loddenkemper, T. and Nelson, C.A. (2017) Nonlinear EEG Biomarker Profiles for Autism and Absence Epilepsy. Neuropsychiatric Electrophysiology, 3, Article No. 1. [Google Scholar] [CrossRef
[16] Mohammadi, M., Al-Azab, F., Raahemi, B., et al. (2015) Data Mining EEG Signals in Depression for Their Diagnostic Value. BMC Medical Informatics and Decision Making, 15, Article No. 108. [Google Scholar] [CrossRef] [PubMed]
[17] Skåtun, K.C., Kaufmann, T., Doan, N.T., et al. (2017) Consistent Functional Connectivity Alterations in Schizophrenia Spectrum Disorder: A Multisite Study. Schizophrenia Bulletin, 43, 914-924. [Google Scholar] [CrossRef] [PubMed]
[18] Dimitriadis, S.I., Liparas, D., Tsolaki, M.N., et al. (2018) Random Forest Feature Selection, Fusion and Ensemble Strategy: Combining Multiple Morphological MRI Measures to Discriminate among Healthy Elderly, MCI, cMCI and Alzheimer’s Disease Patients: From the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Database. Journal of Neuroscience Methods, 302, 14-23. [Google Scholar] [CrossRef] [PubMed]
[19] Koutsouleris, N., Kambeitz-Ilankovic, L., Ruhrmann, S., et al. (2018) Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or with Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis. JAMA Psychiatry, 75, 1156-1172. [Google Scholar] [CrossRef] [PubMed]
[20] Cook, B.L., Progovac, A.M., Chen, P., et al. (2016) Novel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid. Computational and Mathematical Methods in Medicine, 2016, Article ID: 8708434. [Google Scholar] [CrossRef] [PubMed]
[21] Yang, S., Zhou, P., Duan, K., et al. (2018) emHealth: Towards Emotion Health through Depression Prediction and Intelligent Health Recommender System. Mobile Networks and Applications, 23, 216-226. [Google Scholar] [CrossRef
[22] Chalmers, C., Hurst, W., Mackay, M., et al. (2016) A Smart Health Monitoring Technology. In: Huang, D.-S., et al., Eds., International Conference on Intelligent Computing, Springer, Cham, 832-842. [Google Scholar] [CrossRef
[23] Salafi, T. and Kah, J.C.Y. (2015) Design of Unobtrusive Wearable Mental Stress Monitoring Device Using Physiological Sensor. In: Goh, J. and Lim, C.T., Eds., 7th WACBE World Congress on Bioengineering 2015, Springer, Cham, 11-14. [Google Scholar] [CrossRef
[24] Liang, X., Gu, S., Deng, J., et al. (2015) Investigation of College Students’ Mental Health Status via Semantic Analysis of Sina Microblog. Wuhan University Journal of Natural Sciences, 20, 159-164. [Google Scholar] [CrossRef
[25] Li, W., Frank, E., Zhao, Z., et al. (2020) Mental Health of Young Physicians in China during the Novel Coronavirus Disease 2019 Outbreak. JAMA Network Open, 3, e2010705. [Google Scholar] [CrossRef] [PubMed]
[26] Liu, T., Li, S., Qiao, X., et al. (2021) Longitudinal Change of Mental Health among Active Social Media Users in China during the COVID-19 Outbreak. Healthcare (Basel), 9, Article No. 833. [Google Scholar] [CrossRef] [PubMed]
[27] Dipnall, J.F., Pasco, J.A., Berk, M., et al. (2017) Why So GLUMM? Detecting Depression Clusters through Graphing Lifestyle-Environs Using Machine-Learning Methods (GLUMM). European Psychiatry, 39, 40-50. [Google Scholar] [CrossRef] [PubMed]