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

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 suicid...

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Bibliographic Details
Published in:International journal of environmental research and public health 2020-08, Vol.17 (16), p.5929
Main Authors: Bernert, Rebecca A., Hilberg, Amanda M., Melia, Ruth, Kim, Jane Paik, Shah, Nigam H., Abnousi, Freddy
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Behavior
Bipolar disorder
Datasets
Decision trees
Emotional disorders
Evaluation
Language
Learning algorithms
Machine learning
Mood disorders
Natural language processing
Prevention
Principal components analysis
Public health
Research methodology
Review
Risk
Risk factors
Search strategies
Social networks
Studies
Suicide
Suicide prevention
Suicides & suicide attempts
Systematic review
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Summary: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.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph17165929