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Advances in Psychological Science    2020, Vol. 28 Issue (2) : 266-274     DOI: 10.3724/SP.J.1042.2020.00266
Regular Articles |
The application of machine learning in depression
DONG Jianyu, WEI Wenqi, WU Ke, NI Na, WANG Canfei, FU Ying, PENG Xin()
School of nursing, Jilin university, Changchun 130012, China
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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.

Keywords machine learning      depression      prediction      algorithm      model     
:  R395  
Corresponding Authors: Xin PENG     E-mail:
Issue Date: 25 December 2019
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Jianyu DONG
Wenqi WEI
Canfei WANG
Ying FU
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Jianyu DONG,Wenqi WEI,Ke WU, et al. The application of machine learning in depression[J]. Advances in Psychological Science, 2020, 28(2): 266-274.
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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
硬件需求 一般
训练时间 较短
解释性 良好
拟合能力 一般 很强
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