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Data science for mental health: a UK perspective on a global challenge

Research output: Contribution to journalArticle

Andrew M. McIntosh, Robert Stewart, Ann John, Daniel J. Smith, Katrina Davis, Cathie Sudlow, Aiden Corvin, Kristin K. Nicodemus, David Kingdon, Lamiece Hassan, Matthew Hotopf, Stephen M. Lawrie, Tom C. Russ, John R. Geddes, Miranda Wolpert, Eva Wölbert, David J. Porteous

Original languageEnglish
Pages (from-to)993-998
Number of pages6
JournalThe Lancet Psychiatry
Volume3
Issue number10
Early online date28 Sep 2016
DOIs
Publication statusPublished - 1 Oct 2016

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Abstract

Data science uses computer science and statistics to extract new knowledge from high-dimensional datasets (ie, those with many different variables and data types). Mental health research, diagnosis, and treatment could benefit from data science that uses cohort studies, genomics, and routine health-care and administrative data. The UK is well placed to trial these approaches through robust NHS-linked data science projects, such as the UK Biobank, Generation Scotland, and the Clinical Record Interactive Search (CRIS) programme. Data science has great potential as a low-cost, high-return catalyst for improved mental health recognition, understanding, support, and outcomes. Lessons learnt from such studies could have global implications.

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