A deep architecture for depression detection using posting, behavior, and living environment data
| Autor: | Arbee L. P. Chen, Min Yen Wu, En Tzu Wang, Chih-Ya Shen |
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| Rok vydání: | 2018 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Living environment Deep learning 02 engineering and technology Affect (psychology) Interpersonal relationship ComputingMethodologies_PATTERNRECOGNITION Recurrent neural network Artificial Intelligence Hardware and Architecture 020204 information systems 0202 electrical engineering electronic engineering information engineering Social media Artificial intelligence Architecture business Software Depression (differential diagnoses) Information Systems Cognitive psychology |
| Zdroj: | Journal of Intelligent Information Systems. 54:225-244 |
| ISSN: | 1573-7675 0925-9902 |
| DOI: | 10.1007/s10844-018-0533-4 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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