CitationHill, Ryan M.; Oosterhoff, Benjamin; & Do, Calvin (2019). Using machine learning to identify suicide risk: A classification tree approach to prospectively identify adolescent suicide attempters. Archives of Suicide Research. pp. 1-33
AbstractObjective: This study applies classification tree analysis to prospectively identify suicide attempters among a large adolescent community sample, to demonstrate the strengths and limitations of this approach for risk identification. Method: Data were drawn from the National Longitudinal Study of Adolescent to Adult Health. Youth (n=4,834, Mage=16.15, SD=1.63, 52.3% female, 63.7% White) completed at-home interviews at Wave 1 and a measure of suicide attempts 12 months later, at Wave 2. Results: Results indicated two classification tree solutions that maximized risk prediction, with 69.8%/85.7% sensitivity/specificity and 90.6%/70.9% sensitivity/specificity, respectively. Conclusion: Classification trees provide a technique for identification of individuals at-risk for suicide attempts. Classification trees produce easy-to-implement decision rules and tailored screening approaches that can be adapted to the goals of a particular organization.
Reference TypeJournal Article
Journal TitleArchives of Suicide Research
Author(s)Hill, Ryan M.