Background: An estimated 1100 college students die by suicide each year. Our ability to predict who is at risk for suicide, as well as our knowledge of resilience factors protecting against it, remains limited. We used a machine learning (ML) framework in conjunction with a large battery of self-report and demographic measures to select features contributing most to observed variability in suicidal thoughts and behaviors (STBs) in college.Method: First-year university students completed demographic and clinically-relevant self-report measures at the beginning of the first semester of college (baseline; n=356), and at end-of-year (n=228). Suicide Behaviors Questionnaire-Revised (SBQ-R) assessed STBs. A ML pipeline with 55 and 57 variables using stacking and nested cross-validation to avoid overfitting was conducted to examine predictors of baseline and end-of-year STBs, respectively. Results: For baseline SBQ-R score, the identified ML algorithm explained 28.3% of variance (95%CI: 28-28.5%), with depression severity, meaning and purpose in life, and social isolation among the most important predictors. For end-of-year SBQ-R score, the identified algorithm explained 5.6% of variance [95%CI: 5.1-6.1%], with baseline SBQ-R score, emotional suppression, and positive emotional experiences among the most important predictors.Limitations: External validation of the model with another independent sample is needed for further demonstrating its replicability.Conclusions: ML analyses replicated known factors contributing to STBs, and identified novel, potentially modifiable risk and resilience factors. Intervention programing on college campuses aiming to reduce depressive symptomatology, promote positive affect and social connectedness, and foster a sense of meaning and purpose, may be effective in reducing STBs.
The Relationship of Suicidal Thoughts and Behaviors to Sleep Disturbance: a Review of Recent Findings
