基于离散化移动特征的CNN抑郁检测模型

Yueru Yan, Mei Tu, Hongbo Wen
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引用次数: 0

摘要

长期以来,抑郁症一直是一种严重的精神疾病,严重影响着人们的生活质量。与此同时,随着智能手机成为人们生活中不可或缺的一部分,它创造了通过用户使用手机和传感器数据分析用户感受的机会。然而,以往的研究主要采用机器学习方法进行抑郁检测,忽略了其中隐藏的序列模式。在这项研究中,我们的目标是通过从手机及其传感器收集的连续移动数据来监测抑郁症的症状。首先,我们建立了深度学习模型deep- caser,充分利用移动数据中的顺序信息。其次,我们引入了一种基于信息值的离散化方法来处理数据稀疏性和异常值。我们总共招募了257人加入这项研究,并从他们的智能手机和电子手环中提取了为期五天的纵向数据。我们分别进行了两个实验来检验deep -caser和离散化方法的有效性。结果表明,deep- caser优于大多数机器学习方法,离散化进一步提高了深度学习模型的性能,达到了0.83的整体精度。我们的研究表明,采用具有连续电话使用和传感数据的深度学习模型来检测抑郁症是有希望的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A CNN Model with Discretized Mobile Features for Depression Detection
Depression has been a serious mental illness for a long time, which significantly influences people’s life quality. Meanwhile, as the smartphone becomes an integral part of people’s lives, it creates the opportunity to analyze users’ feelings through their phone usage and sensor data. However, previous studies mainly adopt machine-learning methods for depression detection, ignoring the sequential patterns hidden in them. In this study, we aim to monitor the symptoms of depression through sequential mobile data collected from phones and their sensors. First, we establish a deep-learning model called Dep-caser to fully utilize the sequential information in mobile data. Next, we introduce a discretization method based on Information Value to deal with data sparsity and outliers. In total, we recruited 257 people to join the study and extracted five-day longitudinal data from their smartphones and electronic bands. We conduct two experiments to examine the effectiveness of the Dep-caser and discretization method respectively. The results demonstrate that Dep-caser outperforms most of the machine learning methods and the discretization further improves the performance of the deep-learning model to achieve an overall accuracy of 0.83. Our study shows the promising future to adopt deep-learning models with sequential phone usage and sensing data to detect depression.
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