ISSN 1671-3710
CN 11-4766/R

心理科学进展 ›› 2020, Vol. 28 ›› Issue (2): 266-274.doi: 10.3724/SP.J.1042.2020.00266

• 研究前沿 • 上一篇    下一篇


董健宇, 韦文棋, 吴珂, 妮娜, 王粲霏, 付莹, 彭歆()   

  1. 吉林大学护理学院, 长春 130012
  • 收稿日期:2019-04-16 出版日期:2020-02-15 发布日期:2019-12-25
  • 通讯作者: 彭歆

The application of machine learning in depression

DONG Jianyu, WEI Wenqi, WU Ke, NI Na, WANG Canfei, FU Ying, PENG Xin()   

  1. School of nursing, Jilin university, Changchun 130012, China
  • Received:2019-04-16 Online:2020-02-15 Published:2019-12-25
  • Contact: PENG Xin


抑郁症患者疾病意识的不足以及早期筛查方法的缺乏导致患者在被诊断时大多已发展至重性抑郁障碍。为改善现状, 近年来机器学习被逐渐应用到抑郁症的早期预测、早期识别、辅助诊断和治疗决策中。在应用中, 机器学习模型准确性的影响因素包括样本集种类及规模、特征工程、算法类型等。建议未来将机器学习进一步融入医疗健康系统及移动应用程序等, 不断优化机器学习模型, 通过充分挖掘患者健康数据来改善抑郁症的预防、识别、诊断和治疗等相关问题。

关键词: 机器学习, 抑郁症, 预测, 算法, 模型


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