TY - JOUR
T1 - Risk assessment tools and data-driven approaches for predicting and preventing suicidal behavior
AU - Velupillai, Sumithra
AU - Hadlaczky, Gergö
AU - Baca-Garcia, Enrique
AU - Gorrell, Genevieve M.
AU - Werbeloff, Nomi
AU - Nguyen, Dong
AU - Patel, Rashmi
AU - Leightley, Daniel
AU - Downs, Johnny
AU - Hotopf, Matthew
AU - Dutta, Rina
N1 - Publisher Copyright: Copyright © 2019 Velupillai, Hadlaczky, Baca-Garcia, Gorrell, Werbeloff, Nguyen, Patel, Leightley, Downs, Hotopf and Dutta. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Clinical informatics
KW - Machine learning
KW - Natural language processing
KW - Suicidality
KW - Suicide risk assessment
KW - Suicide risk prediction
UR - http://www.scopus.com/inward/record.url?scp=85062732148&partnerID=8YFLogxK
U2 - https://doi.org/10.3389/fpsyt.2019.00036
DO - https://doi.org/10.3389/fpsyt.2019.00036
M3 - مقالة مرجعية
C2 - 30814958
SN - 1664-0640
VL - 10
JO - Frontiers in Psychiatry
JF - Frontiers in Psychiatry
IS - FEB
M1 - 36
ER -
