How data science can advance mental health research

the MQ Data Science group

doi:10.1038/s41562-018-0470-9

How data science can advance mental health research

the MQ Data Science group

Psychology

University of Edinburgh
University of Edinburgh
Royal Edinburgh Hospital
MQ: Transforming Mental Health
King's College London
National Institute for Health Research Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust
RCP National Guideline Centre
University College London
University of Cambridge
Swansea University Medical School
Plymouth University, Peninsula Schools of Medicine and Dentistry
Devon Partnership NHS Trust
University of Stirling
NMAHP Research Unit
Farr Scotland
MRC Centre for Molecular Bacteriology and Infection
Farr Manchester
West of England Academic Health Science Network
Durham University
MHILP
Innovation
Autistica, London
University of Sheffield
Edinburgh Napier University
Newcastle University
University of Oxford
University of Manchester
Farr Wales
University of York
BACP
Ulster University
ESRC
University of Exeter Medical School
Department for Education
HSCIC
Public Health England
University of Glasgow
Farr Institute London
Anna Freud National Centre for Children and Families
Cardiff University

Research output: Contribution to a Journal (Peer & Non Peer) › Article › peer-review

48 Citations (Scopus)

Abstract

Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.

Original language	English
Pages (from-to)	24-32
Number of pages	9
Journal	Nature human behaviour
Volume	3
Issue number	1
DOIs	https://doi.org/10.1038/s41562-018-0470-9
Publication status	Published - 1 Jan 2019

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10.1038/s41562-018-0470-9

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title = "How data science can advance mental health research",

abstract = "Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.",

author = "\{the MQ Data Science group\} and Russ, \{Tom C.\} and Eva Woelbert and Davis, \{Katrina A.S.\} and Hafferty, \{Jonathan D.\} and Zina Ibrahim and Becky Inkster and Ann John and William Lee and Margaret Maxwell and McIntosh, \{Andrew M.\} and Robert Stewart and Margaret Anderson and Kate Aylett and Suzy Bourke and Anna Burhouse and Felicity Callard and Kathy Chapman and Matt Cowley and James Cusack and Jaime Delgadillo and Sophie Dix and Richard Dobson and Gary Donohoe and Nadine Dougall and Johnny Downs and Helen Fisher and Amos Folarin and Thomas Foley and John Geddes and Joardana Globerman and Lamiece Hassan and Joseph Hayes and Helen Hodges and Eddie Jacob and Rowena Jacobs and Cynthia Joyce and Suky Kaur and Maximilian Kerz and James Kirkbride and Gerard Leavey and Glyn Lewis and Keith Lloyd and Wendy Matcham and Erin McCloskey and Andrew McQuillin and Delgado, \{Tamsin Newlove\} and Catherine Newsome and Kristin Nicodemus and David Porteous and Daniel Ray and Simran Sanhu and Daniel Smith and Laura Tutu and Ayath Ullah and Bill Vance and Miranda Wolpert and Cathy Wyse and Stanley Zammit",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Limited.",

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AU - McIntosh, Andrew M.

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AU - Anderson, Margaret

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AU - Bourke, Suzy

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AU - Chapman, Kathy

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AU - Dobson, Richard

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AU - Dougall, Nadine

AU - Downs, Johnny

AU - Fisher, Helen

AU - Folarin, Amos

AU - Foley, Thomas

AU - Geddes, John

AU - Globerman, Joardana

AU - Hassan, Lamiece

AU - Hayes, Joseph

AU - Hodges, Helen

AU - Jacob, Eddie

AU - Jacobs, Rowena

AU - Joyce, Cynthia

AU - Kaur, Suky

AU - Kerz, Maximilian

AU - Kirkbride, James

AU - Leavey, Gerard

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AU - Lloyd, Keith

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AU - Porteous, David

AU - Ray, Daniel

AU - Sanhu, Simran

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AU - Tutu, Laura

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AU - Vance, Bill

AU - Wolpert, Miranda

AU - Wyse, Cathy

AU - Zammit, Stanley

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EP - 32

JO - Nature human behaviour

JF - Nature human behaviour

IS - 1

ER -

How data science can advance mental health research

Abstract

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