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Using big data to advance mental health research
  1. Anne Duffy1,2,
  2. Maria Faurholt-Jepsen3,
  3. Michael Ostacher4
  1. 1 Psychiatry, Queen's University, Kingston, Ontario, Canada
  2. 2 Honorary Member, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
  3. 3 Department of Psychiatry, University of Copenhagen, Kobenhavn, Denmark
  4. 4 Department of Psychiatry, School of Medicine, Stanford University, Stanford, California, USA
  1. Correspondence to Professor Anne Duffy, Psychiatry, Queen's University, Kingston ON K7L 3N6, Canada; anne.duffy{at}queensu.ca

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Over the past decade there has been an increasing awareness of the potential for data science to make important advances in brain and mental health research. This focus has coincided with the use of electronic health records in the clinic, the advent of electronic remote data capture through smart devices, publicly available de-identified large data sets, and the emergence of research consortia collaborating on the analysis of complex data sets. Interest in data science in mental health research has been further heightened by the widely acknowledged need for improved diagnostic precision and individualised risk prediction, long-term monitoring, and treatment. Using big data approaches to tackle complex mental health problems is now felt to be a key research priority moving forward. Therefore, Evidence Based Mental Health has …

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Footnotes

  • Contributors All authors have contributed to this Editorial and accepted the final version for submission and publication.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Commissioned; internally peer reviewed.

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