Gkotsis, George, Oellrich, Anika, Velupillai, Sumithra, Liakata, Maria, Hubbard, Tim J. P., Dobson, Richard J. B. and Dutta, Rina (2017) Characterisation of mental health conditions in social media using Informed Deep Learning. Scientific Reports, 7 . 45141. doi:10.1038/srep45141 ISSN 2045-2322.
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Abstract
The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language
processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand
the patients’ own experiences, and only record cross-sectional, professional impressions at the point of care. Social media platforms have become a source of ‘in the moment’ daily exchange, with topics
including well-being and mental health. In this study, we analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according
to 11 disorder themes. Using a neural network and deep learning approach, we could automatically recognise mental illness-related posts in our balenced dataset with an accuracy of 91.08% and select
the correct theme with a weighted average accuracy of 71.37%. We believe that these results are a first step in developing methods to characterise large amounts of user-generated content that could support content curation and targeted interventions.
| Item Type: | Journal Article |
|---|---|
| Subjects: | R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science |
| Library of Congress Subject Headings (LCSH): | Mental health., Medical records -- Data processing., Social psychiatry. |
| Journal or Publication Title: | Scientific Reports |
| Publisher: | Nature Publishing Group |
| ISSN: | 2045-2322 |
| Official Date: | 22 March 2017 |
| Dates: | Date Event 22 March 2017 Published 15 February 2017 Accepted 22 December 2016 Submitted |
| Volume: | 7 |
| Number of Pages: | 10 |
| Article Number: | 45141 |
| DOI: | 10.1038/srep45141 |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Access rights to Published version: | Open Access (Creative Commons open licence) |
| Date of first compliant deposit: | 21 December 2017 |
| Date of first compliant Open Access: | 21 December 2017 |
| Funder: | Academy of Medical Sciences (Great Britain), e-HOST-IT (Electronic health records to predict HOspitalised Suicide attempts Targeting Information Technology), National Institute for Health Research (Great Britain) (NIHR), Wellcome Trust (London, England) |
| Grant number: | MR/K006584/1) |
| URI: | https://wrap.warwick.ac.uk/87735/ |
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