How data science can advance mental health research

Tom C. Russ (), Eva Woelbert, Katrina A. S. Davis, Jonathan D. Hafferty, Zina Ibrahim, Becky Inkster, Ann John, William Lee, Margaret Maxwell, Andrew M. McIntosh and Rob Stewart
Additional contact information
Tom C. Russ: University of Edinburgh
Eva Woelbert: MQ: Transforming Mental Health
Katrina A. S. Davis: King’s College London
Jonathan D. Hafferty: University of Edinburgh
Zina Ibrahim: King’s College London
Becky Inkster: University of Cambridge
Ann John: King’s College London
William Lee: University of Plymouth
Margaret Maxwell: University of Stirling
Andrew M. McIntosh: University of Edinburgh
Rob Stewart: King’s College London

Nature Human Behaviour, 2019, vol. 3, issue 1, 24-32

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41562-018-0470-9

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