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

Datos Bibliográficos

ID15527209
AutoresSumithra Velupillai (0000-0002-4178-2980, South London and Maudsley NHS Foundation Trust, autor de correspondencia), Gergö Hadlaczky (0000-0002-0556-6244, Karolinska Institutet), Enrique Baca-Garcia (0000-0002-6963-6555, Universidad Autónoma de Madrid), Genevieve Gorrell (0000-0002-8324-606X, University of Sheffield), Genevieve M Gorrell, Nomi Werbeloff (0000-0002-5485-615X, University College London), Dong Nguyen (0000-0002-6062-3117, The Alan Turing Institute), Rashmi Patel (0000-0002-9259-8788, King's College London), Daniel Leightley (0000-0001-9512-752X, King's College London), Johnny Downs (0000-0002-8061-295X, King's College London), Matthew Hotopf (0000-0002-3980-4466, King's College London), Rina Dutta (0000-0002-5614-8659, South London and Maudsley NHS Foundation Trust)
Año2019
Volumen10
Páginas36-36
Fecha de publicación2019-02-13
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFrontiers in Psychiatry (JOURNAL)
Identificadores de la revistaISSN: 1664-0640 • E-ISSN: 1664-0640
EditorialFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2019.00036
PMID30814958
OpenAlexW2913968412
IdiomaEN
Citas recibidas5
Referencias citadas63

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

Computer security · Medical emergency · Poison control · Psychiatry · Risk assessment · Suicidal behavior · Suicidal ideation · Suicide prevention · Clinical Psychology · Computer Science · Digital Mental Health Interventions · Medicine · Mental Health Research Topics · Psychology · Suicide and Self-Harm Studies