A deep architecture for depression detection using posting, behavior, and living environment data-Reference-Cited by-同舟云学术

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

Published:2018-10-30 Issue:2 Volume:54 Page:225-244
ISSN:0925-9902
Container-title:Journal of Intelligent Information Systems
language:en
Short-container-title:J Intell Inf Syst

Author:

Wu Min Yen,Shen Chih-Ya,Wang En Tzu,Chen Arbee L. P.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Link

http://link.springer.com/content/pdf/10.1007/s10844-018-0533-4.pdf

Reference39 articles.

1. Depression Fact Sheet, World Health Organization, http://www.who.int/mediacentre/factsheets/fs369/en/ (2012).

2. Major depressive episode among full-time college students and other young adults, aged 18 to 22 NSDUH report (2012).

3. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5) american psychiatric pub.

4. Andalibi, N., Ozturk, P., Forte, A. (2015). Depression-related imagery on instagram. In ACM conference companion on computer supported cooperative work & social computing (pp. 231–234).

5. Arevian, G. (2007). Recurrent neural networks for robust real-world text classification. In IEEE/WIC/ACM international conference on web intelligence (pp. 326–329).

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