Data science for mental health: a UK perspective on a global challenge. (October 2016)

• Record Type: Journal Article
• Authors: McIntosh, Andrew M; Stewart, Robert; John, Ann; Smith, Daniel J; Davis, Katrina; Sudlow, Cathie; Corvin, Aiden; Nicodemus, Kristin K; Kingdon, David; Hassan, Lamiece; Hotopf, Matthew; Lawrie, Stephen M; Russ, Tom C; Geddes, John R; Wolpert, Miranda; Wölbert, Eva; Porteous, David J
• Is Part Of: Lancet. Volume 3:Number 10(2016)
• Page Start: 993
• Page End: 998
• Publication Date: 2016-10
• Subjects: Psychiatry -- Periodicals; 616.89
• DOI: 10.1016/S2215-0366(16)30089-X
• Languages: English
• ISSNs: 2215-0366

Abstract

Summary: 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.