The application of machine learning in depression

doi:10.3724/SP.J.1042.2020.00266

Abstract

Abstract:

Because of the lack of disease awareness in depressed patients and the lack of early screening methods, most patients had developed to major depressive disorder when they were first diagnosed with depression. In order to improve the current situation, machine learning has been gradually used in some aspects of depression recently years, including early prediction, early recognition, auxiliary diagnosis, and treatment. In the application, the factors that affect the accuracy of machine learning model include the type and size of sample set, feature engineering, algorithm type, etc. In the future, machine learning should be further integrated into the health care system and mobile applications, continuously optimizing the machine learning model, fully mining patient health data to improve depression-related problems in terms of the prevention, identification, diagnosis, treatment and so on.

Key words: machine learning, depression, prediction, algorithm, model

CLC Number:

R395

DONG Jianyu, WEI Wenqi, WU Ke, NI Na, WANG Canfei, FU Ying, PENG Xin. The application of machine learning in depression[J]. Advances in Psychological Science, 2020, 28(2): 266-274.

Figures/Tables 3

References 56

[1]	靳宇倡, 丁美月 . ( 2017). 产后抑郁的预测因素及神经生理机制. 心理科学进展, 25( 7), 1145-1161.
[2]	康环宇 . ( 2018). 抑郁人群语音信号时间特征研究(硕士学位论文). 兰州大学, 兰州.
[3]	李建秀 . ( 2018). 基于抑郁症患者ERP数据的源定位及分类算法研究(硕士学位论文). 兰州大学, 兰州.
[4]	刘岩, 李幼军, 陈萌 . ( 2017). 基于固有模态分解和深度学习的抑郁症脑电信号分类分析. 中国医学物理学杂志, 34( 09), 963-967.
[5]	罗晓舟, 温小鹏, 何家扬, 黄健婷, 唐纯志 . ( 2017). 基于机器学习的卒中后抑郁影响因素分析. 中医杂志, 58( 17), 1478-1481.
[6]	吕瑞雪 . ( 2014). 围产期抑郁症量化诊断系统的研发(硕士学位论文). 华南理工大学, 广州.
[7]	马丽明, 黄昊 . ( 2016). 医疗信息人才的培养模式研究. 中国数字医学, 11(10), 74-75+97.
[8]	任志洪, 李献云, 赵陵波, 余香莲, 李政汉, 赖丽足, … 江光荣 . ( 2016). 抑郁症网络化自助干预的效果及作用机制——以汉化MoodGYM为例. 心理学报, 48( 07), 818-832.
[9]	沈骥 . ( 2015). 基于眼动、脑电特征的抑郁识别研究(硕士学位论文). 兰州大学, 兰州.
[10]	于斌, 牛凯军 . ( 2015). 膳食营养与抑郁症的关系. 心理科学进展, 23( 12), 2107-2117.
[11]	周爱保, 鲁小勇, 吴文意, 徐世鹏 . ( 2017). 采用语音的抑郁症诊断研究述评. 小型微型计算机系统, 38( 11), 2619-2624.
[12]	Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H., & Subha, D. P . ( 2018). Automated EEG-based screening of depression using deep convolutional neural network. Computer Methods and Programs in Biomedicine, 161, 103-113.
[13]	American Psychiatric Association. ( 2013). Diagnostic and statistical manual of mental disorders (DSM-5®). New York: American Psychiatric Pub.
[14]	Bailey, N. W., Hoy, K. E., Rogasch, N. C., Thomson, R. H., McQueen, S., Elliot, D., … Fitzgerald, P. B . ( 2019). Differentiating responders and non-responders to rTMS treatment for depression after one week using resting EEG connectivity measures. Journal of Affective Disorders, 242, 68-79.
[15]	Brundtland, G. H . ( 2001). Mental health: New understanding, new hope. JAMA - Journal of the American Medical Association, 286( 19), 2391-2391.
[16]	Cabitza, F., & Banfi, G . ( 2018). Machine learning in laboratory medicine: Waiting for the flood? Clinical Chemistry and Laboratory Medicine, 56( 4), 516-524.
[17]	Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., … Corlett, P. R . ( 2016). Cross-trial prediction of treatment outcome in depression: A machine learning approach. Lancet Psychiatry, 3( 3), 243-250.
[18]	Dipnall, J. F., Pasco, J. A., Berk, M., Williams, L. J., Dodd, S., Jacka, F. N., & Meyer, D . ( 2016). Into the bowels of depression: Unravelling medical symptoms associated with depression by applying machine-learning techniques to a community based population sample. Plos One, 11( 12), e0167055.
[19]	Enshaeifar, S., Zoha, A., Markides, A., Skillman, S., Acton, S. T., Elsaleh, T., … Barnaghi, P . ( 2018). Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques. Plos One, 13( 5), e0195605.
[20]	Fatima, I., Mukhtar, H., Ahmad, H. F., & Rajpoot, K . ( 2018). Analysis of user-generated content from online social communities to characterise and predict depression degree. Journal of Information Science, 44( 5), 683-695.
[21]	Foster, S., Mohler-Kuo, M., Tay, L., Hothorn, T., & Seibold, H . ( 2019). Estimating patient-specific treatment advantages in the “Treatment for adolescents with depression study”. Journal of Psychiatric Research, 112, 61-70.
[22]	Fu, M. R., Wang, Y., Li, C., Qiu, Z., Axelrod, D., Guth, A. A., … Cheung, Y. K . ( 2018). Machine learning for detection of lymphedema among breast cancer survivors. mHealth, 4, 17-17.
[23]	Gao, S., Calhoun, V. D., & Sui, J . ( 2018). Machine learning in major depression: From classification to treatment outcome prediction. CNS Neuroscience & Therapeutics, 24( 11), 1037-1052.
[24]	Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., & Torresen, J . ( 2018). Mental health monitoring with multimodal sensing and machine learning: A survey. Pervasive and Mobile Computing, 51, 1-26.
[25]	Gold, P. W., Machado-Vieira, R., & Pavlatou, M. G . ( 2015). Clinical and biochemical manifestations of depression: Relation to the neurobiology of stress. Neural Plasticity, 2015, 581976.
[26]	Hasanzadeh, F., Mohebbi, M., & Rostami, R . ( 2019). Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal. Journal of Affective Disorders, 256, 132-142.
[27]	He, L., & Cao, C . ( 2018). Automated depression analysis using convolutional neural networks from speech. Journal of Biomedical Informatics, 83, 103-111.
[28]	Helbich, M., Yao, Y., Liu, Y., Zhang, J., Liu, P., & Wang, R . ( 2019). Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China. Environment International, 126, 107-117.
[29]	Hilbert, K., Lueken, U., Muehlhan, M., & Beesdo-Baum, K . ( 2017). Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study. Brain and Behavior, 7( 3), e00633.
[30]	Hollon, S. D., Cohen, Z. D., Singla, D. R., & Andrews, P. W . ( 2019). Recent developments in the treatment of depression. Behavior Therapy, 50( 2), 257-269.
[31]	Islam, M. R., Kabir, M. A., Ahmed, A., Kamal, A. M., Wang, H., & Ulhaq, A . ( 2018). Depression detection from social network data using machine learning techniques. Health Information Science and Systems, 6, 8.
[32]	Jimenez-Serrano, S., Tortajada, S., & Miguel Garcia-Gomez, J . ( 2015). A mobile health application to predict postpartum depression based on machine learning. Telemedicine and e-Health, 21( 7), 567-574.
[33]	Kantoch, E . ( 2018). Recognition of sedentary behavior by machine learning analysis of wearable sensors during activities of daily living for telemedical assessment of cardiovascular risk. Sensors, 18( 10), 3219.
[34]	Karhade, A.V., Ogink, P. T., Thio, Q. C. B. S., Cha, T. D., Gormley, W. B., Hershman, S. H., … Schwab, J. H . ( 2019). Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation. The Spine Journal. doi: 10.1016/j.spinee.2019.06.002.
[35]	Kolossvary, M., de Cecco, C. N., Feuchtner, G., & Maurovich-Horvat, P . ( 2019). Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning. Journal of Cardiovascular Computed Tomography. doi: 10.1016/j. jcct.2019.04.007.
[36]	Lee, Y., Ragguett, R. M., Mansur, R. B., Boutilier, J. J., Rosenblat, J. D., Trevizol, A., … McIntyre, R. S . ( 2018). Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. Journal of Affective Disorders, 241, 519-532.
[37]	Li, X., La, R., Wang, Y., Niu, J., Zeng, S., Sun, S., & Zhu, J . ( 2019). EEG-based mild depression recognition using convolutional neural network. Medical & Biological Engineering & Computing, 57( 6), 1341-1352.
[38]	Lin, E., Kuo, P.-H., Liu, Y.-L., Yu, Y. W.-Y., Yang, A. C., & Tsai, S.-J . ( 2018). A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers. Frontiers in Psychiatry, 9, 290.
[39]	Lundervold, A.S. & Lundervold, A . ( 2019). An overview of deep learning in medical imaging focusing on MRI. Zeitschrift fur Medizinische Physik, 29( 2), 102-127.
[40]	Marinelarena-Dondena, L., Ferretti, E., Maragoudakis, M., Sapino, M., & Errecalde, M. L . ( 2017). Predicting depression: A comparative study of machine learning approaches based on language usage. Cuadernos De Neuropsicologia-Panamerican Journal of Neuropsychology, 11( 3), 42-54.
[41]	McGinnis, R. S., McGinnis, E. W., Hruschak, J., Lopez- Duran, N. L., Fitzgerald, K., Rosenblum, K. L., & Muzik, Maria . ( 2018). Rapid anxiety and depression diagnosis in young children enabled by wearable sensors and machine learning. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 3983-3986.
[42]	Moreira, M. W. L., Rodrigues, J. J. P. C., Kumar, N., Saleem, K., & Illin, I. V . ( 2019). Postpartum depression prediction through pregnancy data analysis for emotion-aware smart systems updates. Information Fusion, 47, 23-31.
[43]	Mukherjee, H., Obaidullah, S. M., Santosh, K. C., Phadikar, S. & Roy, K . ( 2018). Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal. International Journal of Speech Technology, 21( 4), 753-760.
[44]	Mumtaz, W., & Malik, A. S . ( 2018). A comparative study of different EEG reference choices for diagnosing unipolar depression. Brain Topography, 31( 5), 875-885.
[45]	Patel, M.J., Khalaf, A., & Aizenstein, H.J . ( 2016). Studying depression using imaging and machine learning methods. Neuroimage-Clinical, 10, 115-123.
[46]	Pearson, R., Pisner, D., Meyer, B., Shumake, J., & Beevers, C. G . ( 2018). A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression. Psychological Medicine. doi: 10.1017/ S003329171800315X.
[47]	Ricard, B. J., Marsch, L. A., Crosier, B., & Hassanpour, S . ( 2018). Exploring the utility of community-generated social media content for detecting depression: An analytical study on Instagram. Journal of Medical Internet Research, 20( 12), e11817.
[48]	Sarda, A., Munuswamy, S., Sarda, S., & Subramanian, V . ( 2019). Using passive smartphone sensing for improved risk stratification of patients with depression and diabetes: Cross-sectional observational study. JMIR Mhealth and Uhealth, 7( 1), e11041.
[49]	Sau, A., & Bhakta, I ., ( 2017a). Artificial neural network (ANN) model to predict depression among geriatric population at a slum in Kolkata, India. Journal of Clinical and Diagnostic Research, 11(5), VC01-VC04.
[50]	Sau, A, & Bhakta, I . ( 2017b). Predicting anxiety and depression in elderly patients using machine learning technology. Healthcare Technology Letters, 4( 6), 238-243.
[51]	Senders, J. T., Staples, P. C., Karhade, A. V., Zaki, M. M., Gormley, W. B., Broekman, M. L. D., … Arnaout, O . ( 2018). Machine learning and neurosurgical outcome prediction: A systematic review. World Neurosurgery, 109, 476-486.
[52]	Wallert, J., Gustafson, E., Held, C., Madison, G., Norlund, F., von Essen, L., & Olsson, E. M. G . ( 2018). Predicting adherence to internet-delivered psychotherapy for symptoms of depression and anxiety after myocardial infarction: Machine learning insights from the U-CARE Heart randomized controlled trial. Journal of Medical Internet Research, 20( 10), e10754.
[53]	Wu, C., Dillon, D. G., Hsu, H., Huang, S. A., Barrick, E., & Liu, Y. H . ( 2018). Depression detection using relative EEG power induced by emotionally positive images and a conformal kernel support vector machine. Applied Sciences-Basel, 8( 8), 1244.
[54]	Zhao, K. & So, H . ( 2019). Drug repositioning for schizophrenia and depression/anxiety disorders: A machine learning approach leveraging expression data. IEEE Journal of Biomedical and Health Informatics, 23( 3), 1304-1315.
[55]	Zheng, H., Zheng, P., Zhao, L., Jia, J., Tang, S., Xu, P., … Gao, H . ( 2017). Predictive diagnosis of major depression using NMR-based metabolomics and least-squares support vector machine. Clinica Chimica Acta, 464, 223-227.
[56]	Zilcha-Mano, S., Roose, S. P., Brown, P. J., & Rutherford, B. R . ( 2018). A machine learning approach to identifying placebo responders in late-life depression trials. American Journal of Geriatric Psychiatry, 26( 6), 669-677.

比较内容	传统机器学习	深度学习
主要算法	支持向量机、支持向量机、随机森林、K-近邻算法、浅层人工神经网络…	卷积神经网络、自动编码器、循环神经网络、置信神经网络…
人工提取特征	需要	不需要, 自动抽取特征
数据集	较小	大
硬件需求	一般	高
训练时间	较短	长
解释性	良好	差
拟合能力	一般	很强

比较内容	传统机器学习	深度学习
主要算法	支持向量机、支持向量机、随机森林、K-近邻算法、浅层人工神经网络…	卷积神经网络、自动编码器、循环神经网络、置信神经网络…
人工提取特征	需要	不需要, 自动抽取特征
数据集	较小	大
硬件需求	一般	高
训练时间	较短	长
解释性	良好	差
拟合能力	一般	很强