Frontiers in Psychiatry (Feb 2019)

Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior

  • Sumithra Velupillai,
  • Sumithra Velupillai,
  • Sumithra Velupillai,
  • Gergö Hadlaczky,
  • Gergö Hadlaczky,
  • Enrique Baca-Garcia,
  • Enrique Baca-Garcia,
  • Enrique Baca-Garcia,
  • Enrique Baca-Garcia,
  • Enrique Baca-Garcia,
  • Enrique Baca-Garcia,
  • Enrique Baca-Garcia,
  • Genevieve M. Gorrell,
  • Nomi Werbeloff,
  • Dong Nguyen,
  • Dong Nguyen,
  • Rashmi Patel,
  • Rashmi Patel,
  • Daniel Leightley,
  • Johnny Downs,
  • Johnny Downs,
  • Matthew Hotopf,
  • Matthew Hotopf,
  • Rina Dutta,
  • Rina Dutta

DOI
https://doi.org/10.3389/fpsyt.2019.00036
Journal volume & issue
Vol. 10

Abstract

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Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity for mental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer from low positive predictive values. More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice.

Keywords