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A deep architecture for depression detection using posting, behavior, and living environment data - 2018

A Deep Architecture For Depression Detection Using Posting, Behavior, And Living Environment Data

Research Paper on A Deep Architecture For Depression Detection Using Posting, Behavior, And Living Environment Data

Research Area:  Machine Learning

Abstract:

The World Health Organization (WHO) predicts that depression disorders will be widespread in the next 20 years. These disorders may affect a person-s general health and habits such as altered sleeping and eating patterns in addition to their interpersonal relationships. Early depression detection and prevention therefore becomes an important issue. To address this critical issue, we recruited 1453 individuals who use Facebook frequently and collected their Facebook data. We then propose an automatic depression detection approach, named Deep Learning-based Depression Detection with Heterogeneous Data Sources (D3-HDS), to predict the depression label of an individual by analyzing his/her living environment, behavior, and the posting contents in the social media. The proposed method employs Recurrent Neural Networks to compute the posts representation of each individual. The representations are then combined with other content-based, behavior and living environment features to predict the depression label of the individual with Deep Neural Networks. To our best knowledge, this is the first attempt that simultaneously considers all the content-based, behavior, and living environment features for depression detection. The experiment results on a real dataset show that the performance of our approach significantly outperforms the other baselines.

Keywords:  
Depression Detection
Posting
Behavior
Living Environment Data
Machine Learning
Deep Learning

Author(s) Name:  Min Yen Wu, Chih-Ya Shen, En Tzu Wang & Arbee L. P. Chen

Journal name:  Journal of Intelligent Information Systems volume

Conferrence name:  

Publisher name:  Springer

DOI:  https://doi.org/10.1007/s10844-018-0533-4

Volume Information:  volume 54, pages 225–244 (2020)