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

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

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Data science extracts new knowledge from high dimensional datasets through computer science and statistics. Mental health research, diagnosis and treatment can benefit from data science using consented cohort studies, genomics, routine healthcare and administrative data. The UK is well placed to trial these approaches through well annotated and NHS-linked data science projects, such as 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 how mental health problems may be better recognised, understood, supported and outcomes improved. Lessons learnt from such studies have the potential for global reach in terms of both their output and impact.
Original languageEnglish
Pages (from-to)993-998
Number of pages6
JournalThe Lancet Psychiatry
Issue number10
Early online date1 Oct 2016
Publication statusPublished - Oct 2016


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