Next Article in Journal
Tendency to Worry and Fear of Mental Health during Italy’s COVID-19 Lockdown
Next Article in Special Issue
Transcultural Differences in Risk Factors and in Triggering Reasons of Suicidal and Self-Harming Behaviour in Young People with and without a Migration Background
Previous Article in Journal
Validation of Obesity Status Based on Self-Reported Data among Filipina and Indonesian Female Migrant Domestic Workers in Macao (SAR), China
Previous Article in Special Issue
Effectiveness of Internet- and Mobile-Based Cognitive Behavioral Therapy to Reduce Suicidal Ideation and Behaviors: Protocol for a Systematic Review and Meta-Analysis of Individual Participant Data
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 17
Issue 16
10.3390/ijerph17165929
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

by
Rebecca A. Bernert
1,*,
Amanda M. Hilberg
1,
Ruth Melia
1,2,
Jane Paik Kim
1,
Nigam H. Shah
3,4,† and
Freddy Abnousi
5,6,†
1
Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
2
Department of Psychology, National University of Ireland, Galway, Ireland
3
Department of Medicine, Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA 94304, USA
4
Informatics, Stanford Center for Clinical and Translational Research, and Education (Spectrum), Stanford University, Stanford CA 94304, USA
5
Facebook, Menlo Park, CA 94025, USA
6
Yale University School of Medicine, New Haven, CT 06510, USA
*
Author to whom correspondence should be addressed.
†
Indicates Co-Senior Authorship.
Int. J. Environ. Res. Public Health 2020, 17(16), 5929; https://doi.org/10.3390/ijerph17165929
Submission received: 20 July 2020 / Accepted: 28 July 2020 / Published: 15 August 2020
(This article belongs to the Special Issue Suicidal Behavior as a Complex Dynamical System)
Browse Figures
Versions Notes

Abstract

Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A systematic review of ML investigations evaluating suicidal behaviors was conducted using PubMed/MEDLINE, PsychInfo, Web-of-Science, and EMBASE, employing search strings and MeSH terms relevant to suicide and AI. Databases were supplemented by hand-search techniques and Google Scholar. Inclusion criteria: (1) journal article, available in English, (2) original investigation, (3) employment of AI/ML, (4) evaluation of a suicide risk outcome. N = 594 records were identified based on abstract search, and 25 hand-searched reports. N = 461 reports remained after duplicates were removed, n = 316 were excluded after abstract screening. Of n = 149 full-text articles assessed for eligibility, n = 87 were included for quantitative synthesis, grouped according to suicide behavior outcome. Reports varied widely in methodology and outcomes. Results suggest high levels of risk classification accuracy (>90%) and Area Under the Curve (AUC) in the prediction of suicidal behaviors. We report key findings and central limitations in the use of AI/ML frameworks to guide additional research, which hold the potential to impact suicide on broad scale.
Keywords: artificial intelligence; machine learning; suicide; prediction; risk; intervention artificial intelligence; machine learning; suicide; prediction; risk; intervention

Share and Cite

MDPI and ACS Style

Bernert, R.A.; Hilberg, A.M.; Melia, R.; Kim, J.P.; Shah, N.H.; Abnousi, F. Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations. Int. J. Environ. Res. Public Health 2020, 17, 5929. https://doi.org/10.3390/ijerph17165929

AMA Style

Bernert RA, Hilberg AM, Melia R, Kim JP, Shah NH, Abnousi F. Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations. International Journal of Environmental Research and Public Health. 2020; 17(16):5929. https://doi.org/10.3390/ijerph17165929

Chicago/Turabian Style

Bernert, Rebecca A., Amanda M. Hilberg, Ruth Melia, Jane Paik Kim, Nigam H. Shah, and Freddy Abnousi. 2020. "Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations" International Journal of Environmental Research and Public Health 17, no. 16: 5929. https://doi.org/10.3390/ijerph17165929

APA Style

Bernert, R. A., Hilberg, A. M., Melia, R., Kim, J. P., Shah, N. H., & Abnousi, F. (2020). Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations. International Journal of Environmental Research and Public Health, 17(16), 5929. https://doi.org/10.3390/ijerph17165929

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop