TY  - JOUR
PY  - 2021//
TI  - A machine learning analysis of risk and protective factors of suicidal thoughts and behaviors in college students
JO  - Journal of American college health
A1  - Kirlic, Namik
A1  - Akeman, Elisabeth
A1  - DeVille, Danielle C.
A1  - Yeh, Hung-Wen
A1  - Cosgrove, Kelly T.
A1  - McDermott, Timothy J.
A1  - Touthang, James
A1  - Clausen, Ashley
A1  - Paulus, Martin P.
A1  - Aupperle, Robin L.
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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>
LA  - en
SN  - 0744-8481
UR  - http://dx.doi.org/10.1080/07448481.2021.1947841
ID  - ref1
ER  -