Type

Journal Article

Authors

David J Porteous
Eva Wölbert
Miranda Wolpert
John R Geddes
Tom C Russ
Stephen M Lawrie
Matthew Hotopf
Lamiece Hassan
David Kingdon
Kristin K Nicodemus
and 7 others

Subjects

Medicine & Nursing

Topics
computer science low cost health care mental health global health linked data health research cohort studies

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

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.
Collections Ireland -> Trinity College Dublin -> PubMed

Full list of authors on original publication

David J Porteous, Eva Wölbert, Miranda Wolpert, John R Geddes, Tom C Russ, Stephen M Lawrie, Matthew Hotopf, Lamiece Hassan, David Kingdon, Kristin K Nicodemus and 7 others

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