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Predicting Depression from Internet Behaviors by Timefrequency Features
Zhu, CY (Zhu, Changye)1; Li, BB (Li, Baobin)1; Li, A (Li, Ang)2; Zhu, TS (Zhu, Tingshao)3
第一作者Zhu, CY (Zhu, Changye)
2016-10
会议名称IEEE/WIC/ACM International Conference on Web Intelligence (WI)
通讯作者邮箱tszhu@psych.ac.cn
会议录名称2016 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2016)
卷号不详
期号不详
页码383-390
会议日期OCT 13-16, 2016
会议地点Omaha, NE
摘要

Early detection of depression is important to improve human well-being. This paper proposes a new method to detect depression through time-frequency analysis of Internet behaviors. We recruited 728 postgraduate students and obtained their scores on a depression questionnaire (Zung Selfrating Depression Scale, SDS) and digital records of Internet behaviors. By time-frequency analysis, we built classification models for differentiating higher SDS group from lower group and prediction models for identifying mental status of depressed group more precisely. Experimental results show classification and prediction models work well, and time-frequency features are effective in capturing the changes of mental health status. Results of this paper might be useful to improve the performance of public mental health services.

DOI10.1109/WI.2016.59
语种英语
引用统计
文献类型会议论文
条目标识符http://ir.psych.ac.cn/handle/311026/26554
专题社会与工程心理学研究室
作者单位1.Univ Chinese Acad Sci, Sch Comp & Control, Beijing 100190, Peoples R China
2.Beijing Forestry Univ, Dept Psychol, Beijing 100083, Peoples R China
3.Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714
Zhu, CY ,Li, BB ,Li, A ,et al. Predicting Depression from Internet Behaviors by Timefrequency Features[C],2016:383-390.
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