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How data science can advance mental health research

Russ, Tom C.; Woelbert, Eva; Davis, Katrina A. S.; Hafferty, Jonathan D.; Ibrahim, Zina; Inkster, Becky; John, Ann; Lee, William; Maxwell, Margaret; McIntosh, Andrew M.; Stewart, Rob; MQ Data Science Group


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

Rob Stewart

MQ Data Science Group



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

Journal Article Type Article
Acceptance Date Oct 11, 2018
Online Publication Date Dec 10, 2018
Publication Date Dec 10, 2018
Deposit Date Dec 11, 2018
Publicly Available Date Jun 11, 2019
Journal Nature Human Behaviour
Print ISSN 2397-3374
Publisher Nature Research
Peer Reviewed Peer Reviewed
Keywords Data science, mental health, data mining ,
Public URL
Contract Date Dec 13, 2018