Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps.

Journal: Current psychiatry reports
Published Date: Jun 28, 2018

Abstract

PURPOSE OF REVIEW: As rates of suicide continue to rise, there is urgent need for innovative approaches to better understand, predict, and care for those at high risk of suicide. Numerous mobile and sensor technology solutions have already been proposed, are in development, or are already available today. This review seeks to assess their clinical evidence and help the reader understand the current state of the field.

Authors

  • John Torous
    Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Mark E Larsen
    Black Dog Institute, University of New South Wales, Sydney, New South Wales, Australia.
  • Colin Depp
    Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
  • Theodore D Cosco
    Oxford Institute of Population Ageing, University of Oxford, Oxford, UK.
  • Ian Barnett
    Department of Biostatistics, University of Pennsylvania, Philadelphia, PA, USA.
  • Matthew K Nock
    Department of Psychology, Harvard University, Cambridge, MA, USA.
  • Joe Firth
    NICM Health Research Institute, School of Science and Health, University of Western Sydney, Sydney, Australia.

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