DeepMood
Name
p715-suhara.pdf
Description
Published version
Size
2.59 MB
Format
Adobe PDF
Checksum (MD5)
cec51189ab7a01c5267e70dc163697e5
Author(s)
Suhara, Yoshihiko
Xu, Yinzhan
Pentland, Alex 'Sandy'
Alternative Title
Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks
Date Issued
April 3, 2017
Publisher
International World Wide Web Conferences Steering Committee
Citation
Suhara, Yoshihiko, Xu, Yinzhan and Pentland, Alex 'Sandy'. 2017. "DeepMood."
Version
Final published version
Abstract
© 2017 International World Wide Web Conference Committee (IW3C2) Depression is a prevailing issue and is an increasing problem in many people’s lives. Without observable diagnostic criteria, the signs of depression may go unnoticed, resulting in high demand for detecting depression in advance automatically. This paper tackles the challenging problem of forecasting severely depressed moods based on self-reported histories. Despite the large amount of research on understanding individual moods including depression, anxiety, and stress based on behavioral logs collected by pervasive computing devices such as smartphones, forecasting depressed moods is still an open question. This paper develops a recurrent neural network algorithm that incorporates categorical embedding layers for forecasting depression. We collected large-scale records from 2,382 self-declared depressed people to conduct the experiment. Experimental results show that our method forecast the severely depressed mood of a user based on self-reported histories, with higher accuracy than SVM. The results also showed that the long-term historical information of a user improves the accuracy of forecasting depressed mood.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3038912.3052676
