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机器学习在抑郁症领域的应用

本站小编 Free考研考试/2022-01-01

董健宇, 韦文棋, 吴珂, 妮娜, 王粲霏, 付莹, 彭歆()
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E-mail:pengxin2016@163.com



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
Received:2019-04-16Online:2020-02-15Published:2019-12-25
Contact:PENG Xin E-mail:pengxin2016@163.com






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。



图1深度学习与机器学习的关系
图1深度学习与机器学习的关系



图2基于ML建立抑郁症预测模型的思路框架如图
图2基于ML建立抑郁症预测模型的思路框架如图


表1传统机器学习与深度学习的比较
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

表1传统机器学习与深度学习的比较
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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