Citation

Velupillai S, Hadlaczky G, Baca-Garcia E, Gorrell GM, Werbeloff N, Nguyen D, Patel R, Leightley D, Downs J, Hotopf M, Dutta R. Front. Psychiatry 2019; 10: e36.

Affiliation

South London and Maudsley NHS Foundation Trust, London, United Kingdom.

Copyright

(Copyright © 2019, Frontiers Media)

DOI

10.3389/fpsyt.2019.00036

PMID

30814958

PMCID

PMC6381841

Abstract

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.

Language: en

Keywords

clinical informatics; machine learning; natural language processing; suicidality; suicide risk assessment; suicide risk prediction