A Deep Architecture for Depression Detection using Posting, Behavior, and Living Environment Data

Speaker:        Professor Arbee L.P. Chen
                Chair Professor of Computer Science and
                Information Engineering
                Asia University, Taiwan

Title:          A Deep Architecture for Depression Detection using Posting,
                Behavior, and Living Environment Data

Date:           Friday, 8 September 2017

Time:           11:00am to 12 noon

Venue:          Room 5619 (via lift nos. 31/32), HKUST


Abstract:

The World Health Organization (WHO) predicts that depression disorders
will be widespread in the next 20 years. According to the research, 40% of
the patients with depression have suicidal thoughts and 10%~15% die by
suicide. Early depression detection and prevention therefore becomes an
important issue. In this talk, I will present our approach 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.
The experiment results on a real dataset show that the performance of our
approach significantly outperforms the other baselines.


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Biography:

Arbee L.P. Chen received a Ph.D. degree in computer engineering from the
University of Southern California, USA, and is currently Chair Professor
of Computer Science and Vice President at Asia University, Taiwan. He also
holds joint faculty positions at National Tsing Hua University and
Academia Sinica, Taiwan. Dr. Chen was a Professor of the Department of
Computer Science, National Tsing Hua University; a Member of Technical
Staff at Bell Communications Research, USA; and a Research Scientist at
Unisys, USA. Dr. Chen organized IEEE Data Engineering Conference in
Taiwan, and continuously serves in various capacities for international
conferences and journals. He was invited to deliver a speech in the
NSF-sponsored Inaugural International Symposium on Music Information
Retrieval and the IEEE Shannon Lecture Series, USA, and the Institute for
Advanced Study of Hong Kong University of Science and Technology, Hong
Kong. Dr. Chen's current research interests include big data analytics,
top-k queries, and multimedia information retrieval. He has published more
than 250 papers in renowned international journals and conference
proceedings, and was a visiting scholar at Tsinghua University, China,
Kyoto University, Japan, King's College London, UK, Stanford University,
Boston University, Harvard University, USA, and Hong Kong University of
Science and Technology, Hong Kong.