Research Area:  Machine Learning

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 percent and select the correct theme with a weighted average accuracy of 71.37 percent. 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.

Keywords:  

Author(s) Name:  George Gkotsis, Anika Oellrich, Sumithra Velupillai, Maria Liakata, Tim J. P. Hubbard, Richard J. B. Dobson & Rina Dutta

Publisher name:  Nature

DOI:  https://doi.org/10.1038/srep45141

Volume Information:  

Paper Link:   https://www.nature.com/articles/srep45141#Sec17