@article{ref1,
title="A machine learning analysis of risk and protective factors of suicidal thoughts and behaviors in college students",
journal="Journal of American college health",
year="2021",
author="Kirlic, Namik and Akeman, Elisabeth and DeVille, Danielle C. and Yeh, Hung-Wen and Cosgrove, Kelly T. and McDermott, Timothy J. and Touthang, James and Clausen, Ashley and Paulus, Martin P. and Aupperle, Robin L.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="OBJECTIVE: To identify robust and reproducible factors associated with suicidal thoughts and behaviors (STBs) in college students. <br><br>METHODS: 356 first-year university students completed a large battery of demographic and clinically-relevant self-report measures during the first semester of college and end-of-year (n = 228). Suicide Behaviors Questionnaire-Revised (SBQ-R) assessed STBs. A machine learning (ML) pipeline using stacking and nested cross-validation examined correlates of SBQ-R scores. <br><br>RESULTS: 9.6% of students were identified at significant STBs risk by the SBQ-R. The ML algorithm explained 28.3% of variance (95%CI: 28-28.5%) in baseline SBQ-R scores, with depression severity, social isolation, meaning and purpose in life, and positive affect among the most important factors. There was a significant reduction in STBs at end-of-year with only 1.8% of students identified at significant risk. <br><br>CONCLUSION: Analyses replicated known factors associated with STBs during the first semester of college and identified novel, potentially modifiable factors including positive affect and social connectedness.   Keywords: Social Transition<p /> <p>Language: en</p>",
language="en",
issn="0744-8481",
doi="10.1080/07448481.2021.1947841",
url="http://dx.doi.org/10.1080/07448481.2021.1947841"
}