| Literature DB >> 27042689 |
Mike Conway1, Daniel O'Connor2.
Abstract
Mental health (including substance abuse) is the fifth greatest contributor to the global burden of disease, with an economic cost estimated to be US $2.5 trillion in 2010, and expected to double by 2030. Developing information systems to support and strengthen population-level mental health monitoring forms a core part of the World Health Organization's Comprehensive Action Plan 2013-2020. In this paper, we review recent work that utilizes social media "big data" in conjunction with associated technologies like natural language processing and machine learning to address pressing problems in population-level mental health surveillance and research, focusing both on technological advances and core ethical challenges.Entities:
Year: 2016 PMID: 27042689 PMCID: PMC4815031 DOI: 10.1016/j.copsyc.2016.01.004
Source DB: PubMed Journal: Curr Opin Psychol ISSN: 2352-250X