Literature DB >> 27692269

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

Andrew M McIntosh1, Robert Stewart2, Ann John3, Daniel J Smith4, Katrina Davis2, Cathie Sudlow5, Aiden Corvin6, Kristin K Nicodemus7, David Kingdon8, Lamiece Hassan9, Matthew Hotopf2, Stephen M Lawrie5, Tom C Russ5, John R Geddes10, Miranda Wolpert11, Eva Wölbert12, David J Porteous7.   

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.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2016        PMID: 27692269     DOI: 10.1016/S2215-0366(16)30089-X

Source DB:  PubMed          Journal:  Lancet Psychiatry        ISSN: 2215-0366            Impact factor:   27.083


  18 in total

Review 1.  Uncovering the Genetic Architecture of Major Depression.

Authors:  Andrew M McIntosh; Patrick F Sullivan; Cathryn M Lewis
Journal:  Neuron       Date:  2019-04-03       Impact factor: 17.173

2.  Linking health and education data to plan and evaluate services for children.

Authors:  Johnny Downs; Ruth Gilbert; Richard D Hayes; Matthew Hotopf; Tamsin Ford
Journal:  Arch Dis Child       Date:  2017-01-27       Impact factor: 3.791

3.  Predictive Psychiatric Genetic Testing in Minors: An Exploration of the Non-Medical Benefits.

Authors:  Arianna Manzini; Danya F Vears
Journal:  J Bioeth Inq       Date:  2017-12-11       Impact factor: 1.352

4.  The 'cognitive footprint' of psychiatric and neurological conditions: cross-sectional study in the UK Biobank cohort.

Authors:  B Cullen; D J Smith; I J Deary; J J Evans; J P Pell
Journal:  Acta Psychiatr Scand       Date:  2017-04-07       Impact factor: 6.392

5.  Self-reported medication use validated through record linkage to national prescribing data.

Authors:  Jonathan D Hafferty; Archie I Campbell; Lauren B Navrady; Mark J Adams; Donald MacIntyre; Stephen M Lawrie; Kristin Nicodemus; David J Porteous; Andrew M McIntosh
Journal:  J Clin Epidemiol       Date:  2017-10-31       Impact factor: 7.407

6.  Big data: what it can and cannot achieve.

Authors: 
Journal:  BJPsych Adv       Date:  2018-06-06

7.  Co-development of a Best Practice Checklist for Mental Health Data Science: A Delphi Study.

Authors:  Elizabeth J Kirkham; Catherine J Crompton; Matthew H Iveson; Iona Beange; Andrew M McIntosh; Sue Fletcher-Watson
Journal:  Front Psychiatry       Date:  2021-06-10       Impact factor: 4.157

Review 8.  The Emerging Neurobiology of Bipolar Disorder.

Authors:  Paul J Harrison; John R Geddes; Elizabeth M Tunbridge
Journal:  Trends Neurosci       Date:  2017-11-20       Impact factor: 13.837

9.  Using flawed, uncertain, proximate and sparse (FUPS) data in the context of complexity: learning from the case of child mental health.

Authors:  Miranda Wolpert; Harry Rutter
Journal:  BMC Med       Date:  2018-06-13       Impact factor: 8.775

10.  Perceptions of anonymised data use and awareness of the NHS data opt-out amongst patients, carers and healthcare staff.

Authors:  C Atkin; B Crosby; K Dunn; G Price; E Marston; C Crawford; M O'Hara; C Morgan; M Levermore; S Gallier; S Modhwadia; J Attwood; S Perks; A K Denniston; G Gkoutos; R Dormer; A Rosser; A Ignatowicz; H Fanning; E Sapey
Journal:  Res Involv Engagem       Date:  2021-06-14
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