Using Machine Learning to Identify Suicide Risk: A Classification Tree Approach to Prospectively Identify Adolescent Suicide Attempters.
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. 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 indicated two classification tree solutions that maximized risk prediction, with 69.8%/85.7% sensitivity/specificity and 90.6%/70.9% sensitivity/specificity, respectively. 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.
['Hill|Ryan M|RM|', 'Oosterhoff|Benjamin|B|', 'Do|Calvin|C|']
D000293:Adolescent D005260:Female D006801:Humans D007175:Impulsive Behavior D008137:Longitudinal Studies D000069550:Machine Learning D008297:Male D010552:Personality Assessment D018570:Risk Assessment D012307:Risk Factors D013406:Suicide, Attempted
machine learning adolescent suicide attempt classification tree analysis