research-article

DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks

Authors:

Yoshihiko Suhara,

Alex 'Sandy' PentlandAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 715 - 724

https://doi.org/10.1145/3038912.3052676

Published: 03 April 2017 Publication History

Abstract

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.

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Zhang LChen P(2024)A neural network paradigm for modeling psychometric data and estimating IRT model parameters: Cross estimation networkBehavior Research Methods10.3758/s13428-024-02406-356:7(7026-7058)Online publication date: 12-Apr-2024
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Index Terms

DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing

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cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 03 April 2017

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WWW '17

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WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

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WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Zhang LChen P(2024)A neural network paradigm for modeling psychometric data and estimating IRT model parameters: Cross estimation networkBehavior Research Methods10.3758/s13428-024-02406-356:7(7026-7058)Online publication date: 12-Apr-2024
https://doi.org/10.3758/s13428-024-02406-3
Liu YSoto-Breceda ACook MKaroly PGrayden DKuhlmann LFreestone DSchmidt D(2024)Forecasting events in multidimensional electroencephalographic brain data: Application to epileptic seizure prediction.2024 27th International Conference on Information Fusion (FUSION)10.23919/FUSION59988.2024.10706283(1-8)Online publication date: 8-Jul-2024
https://doi.org/10.23919/FUSION59988.2024.10706283
Neupane SSaha MAli NHnat TSamiei SNandugudi AAlmeida DKumar S(2024)Momentary Stressor Logging and Reflective Visualizations: Implications for Stress Management with WearablesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642662(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642662
AbdElminaam DAshraf MYehia KLoay ARaafat D(2024)Global Mental Health Disorder Trends: Advanced Forecasting Techniques Using Time Series and Neural Networks2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)10.1109/MIUCC62295.2024.10783591(273-280)Online publication date: 13-Nov-2024
https://doi.org/10.1109/MIUCC62295.2024.10783591
Tang YZhang DLiang WLi KLi K(2024)Uncovering Malicious Accounts in Open Mobile Social Networks Using a Graph- and Text-Based Attention Fusion AlgorithmIEEE Internet of Things Journal10.1109/JIOT.2024.341655611:19(31040-31052)Online publication date: 1-Oct-2024
https://doi.org/10.1109/JIOT.2024.3416556
Sahoo SShende CHossain MPatel PWang XMahmud MBi JKamath JRussell ASong DWang B(2024)Using Mobile Daily Mood and Anxiety Self-ratings to Predict Depression Symptom Improvement2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)10.1109/CHASE60773.2024.00012(13-24)Online publication date: 19-Jun-2024
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Prajwal MRaj ASen SSaha SGhosh S(2023)Towards Efficient Emotion Self-report Collection Using Human-AI CollaborationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962697:2(1-23)Online publication date: 12-Jun-2023
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