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Classifying depressed users with multiple instance learning from social network data

Research output: Chapter in Book/Report/Conference proceedingConference paper

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)9781538653777
DOIs
Publication statusPublished - 24 Jul 2018
Event6th IEEE International Conference on Healthcare Informatics, ICHI 2018 - New York, United States
Duration: 4 Jun 20187 Jun 2018

Conference

Conference6th IEEE International Conference on Healthcare Informatics, ICHI 2018
CountryUnited States
CityNew York
Period4/06/20187/06/2018

King's Authors

Abstract

Over 320 million people are suffering from depression worldwide. Depression is one of the common mental health disorders. By its nature, depression can reoccur. People suffering from depression tend to lose interest, have low mood, feel hopeless, or have social isolation. At its worst, depression can lead to suicide. So far, there are a few numbers of studies investigating deep learning techniques to classify social network users with depression. Most of the studies used classical machine learning techniques e.g., regression, support vector machine, or decision trees. This paper aims to develop a deep learning predictive model to classify users with depression. Because depression is a recurrent disease, it is interesting in finding unusual patterns in user-generated content over time. Social network posts over time were extracted for time series data. The predictive model for the classification was obtained from deep learning techniques.

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