Gkotsis, G;
Oellrich, A;
Velupillai, S;
Liakata, M;
Hubbard, TJP;
Dobson, RJB;
Dutta, R;
(2017)
Characterisation of mental health conditions in social media using Informed Deep Learning.
SCIENTIFIC REPORTS
, 7
, Article 45141. 10.1038/srep45141.
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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.
| Type: | Article |
|---|---|
| Title: | Characterisation of mental health conditions in social media using Informed Deep Learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/srep45141 |
| Publisher version: | http://dx.doi.org/10.1038/srep45141 |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1551596 |
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