机器学习在抑郁症领域的应用

doi:10.3724/SP.J.1042.2020.00266

摘要/Abstract

摘要：

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

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

中图分类号:

R395

董健宇, 韦文棋, 吴珂, 妮娜, 王粲霏, 付莹, 彭歆. (2020). 机器学习在抑郁症领域的应用. 心理科学进展 , 28(2), 266-274.

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

图/表 3

参考文献 56

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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	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