@article{ref1,
title="Artificial intelligence and suicide prevention: a systematic review of machine learning investigations",
journal="International journal of environmental research and public health",
year="2020",
author="Bernert, Rebecca A. and Hilberg, Amanda M. and Melia, Ruth and Kim, Jane Paik and Shah, Nigam H. and Abnousi, Freddy",
volume="17",
number="16",
pages="e5929-e5929",
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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="1661-7827",
doi="10.3390/ijerph17165929",
url="http://dx.doi.org/10.3390/ijerph17165929"
}