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

McIntosh, A. M. et al. (2016) Data science for mental health: a UK perspective on a global challenge. Lancet Psychiatry, 3(10), pp. 993-998. (doi:10.1016/S2215-0366(16)30089-X)

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Data science uses computer science and statistics to extract new knowledge from high-dimensional datasets (ie, those with many diff erent variables and data types). Mental health research, diagnosis, and treatment could benefi t 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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Smith, Professor Daniel
Authors: McIntosh, A. M., Stewart, R., John, A., Smith, D. J., Davis, K., Corvin, A., Nicodemus, K. K., Kingdon, D., Hassan, L., Hotopf, M., Lawrie, S. M., Russ, T. C., Geddes, J. R., Wolpert, M., Wolbert, E., and Porteous, D. J.
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > Mental Health and Wellbeing
Journal Name:Lancet Psychiatry
ISSN (Online):2215-0374
Published Online:28 September 2016
Copyright Holders:Copyright © 2016 Elsevier
First Published:First published in Lancet Psychiatry 3(10):993-998
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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