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Data science for mental health: a UK perspective on a global challenge
- Source :
- McIntosh, A M, Stewart, R, John, A, Smith, D J, Davis, K, Sudlow, C, Corvin, A, Nicodemus, K K, Kingdon, D, Hassan, L, Hotopf, M, Lawrie, S M, Russ, T C, Geddes, J R, Wolpert, M, Wölbert, E & Porteous, D J 2016, ' Data science for mental health : a UK perspective on a global challenge ', The Lancet Psychiatry, vol. 3, no. 10, pp. 993-998 . https://doi.org/10.1016/S2215-0366(16)30089-X, McIntosh, A, Stewart, R, John, A, Smith, D J, Davis, K, Sudlow, C, Corvin, A, Nicodemus, K, Kingdon, D, Hassan, L, Hotopf, M, Lawrie, S, Russ, T, Geddes, J R, Wolpert, M, Wölbert, E & Porteous, D 2016, ' Data science for mental health – a UK perspective on a global challenge ', The Lancet Psychiatry, vol. 3, no. 10, pp. 993-998 . https://doi.org/10.1016/S2215-0366(16)30089-X
- Publication Year :
- 2016
- Publisher :
- Elsevier, 2016.
-
Abstract
- Data science uses computer science and statistics to extract new knowledge from high-dimensional datasets (ie, those\ud with many diff erent variables and data types). Mental health research, diagnosis, and treatment could benefi t from\ud data science that uses cohort studies, genomics, and routine health-care and administrative data. The UK is well\ud placed to trial these approaches through robust NHS-linked data science projects, such as the UK Biobank, Generation\ud Scotland, and the Clinical Record Interactive Search (CRIS) programme. Data science has great potential as a low-cost,\ud high-return catalyst for improved mental health recognition, understanding, support, and outcomes. Lessons learnt\ud from such studies could have global implications.
- Subjects :
- Computer science
Perspective (graphical)
Summary data
MEDLINE
Global Health
Data science
Mental health
Data type
Biobank
United Kingdom
3. Good health
03 medical and health sciences
Psychiatry and Mental health
Mental Health
0302 clinical medicine
Global health
Data Mining
Humans
030212 general & internal medicine
030217 neurology & neurosurgery
Biological Psychiatry
Cohort study
Subjects
Details
- Language :
- English
- ISSN :
- 22150366
- Database :
- OpenAIRE
- Journal :
- McIntosh, A M, Stewart, R, John, A, Smith, D J, Davis, K, Sudlow, C, Corvin, A, Nicodemus, K K, Kingdon, D, Hassan, L, Hotopf, M, Lawrie, S M, Russ, T C, Geddes, J R, Wolpert, M, Wölbert, E & Porteous, D J 2016, ' Data science for mental health : a UK perspective on a global challenge ', The Lancet Psychiatry, vol. 3, no. 10, pp. 993-998 . https://doi.org/10.1016/S2215-0366(16)30089-X, McIntosh, A, Stewart, R, John, A, Smith, D J, Davis, K, Sudlow, C, Corvin, A, Nicodemus, K, Kingdon, D, Hassan, L, Hotopf, M, Lawrie, S, Russ, T, Geddes, J R, Wolpert, M, Wölbert, E & Porteous, D 2016, ' Data science for mental health – a UK perspective on a global challenge ', The Lancet Psychiatry, vol. 3, no. 10, pp. 993-998 . https://doi.org/10.1016/S2215-0366(16)30089-X
- Accession number :
- edsair.doi.dedup.....120534115f3279bb72e623b0e81bf038
- Full Text :
- https://doi.org/10.1016/S2215-0366(16)30089-X