Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning.

Journal: Frontiers in psychiatry
Published Date:

Abstract

BACKGROUND: Traditional approaches to modeling suicide-related thoughts and behaviors focus on few data types from often-siloed disciplines. While psychosocial aspects of risk for these phenotypes are frequently studied, there is a lack of research assessing their impact in the context of biological factors, which are important in determining an individual's fulsome risk profile. To directly test this biopsychosocial model of suicide and identify the relative importance of predictive measures when considered together, a transdisciplinary, multivariate approach is needed. Here, we systematically review the emerging literature on large-scale studies using machine learning to integrate measures of psychological, social, and biological factors simultaneously in the study of suicide.

Authors

  • Earvin S Tio
    Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
  • Melissa C Misztal
    Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
  • Daniel Felsky
    Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.

Keywords

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