How data science can advance mental health research

Tom 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, Gerard Leavey

    Research output: Contribution to journalArticlepeer-review

    32 Citations (Scopus)
    73 Downloads (Pure)


    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 languageEnglish
    Pages (from-to)24-32
    Number of pages9
    JournalEuropean Journal of Human Genetics
    Issue number1
    Early online date10 Dec 2018
    Publication statusPublished (in print/issue) - Jan 2019


    • Computational biology and bioinformatics
    • medical research
    • psychology


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