@article{ref1,
title="Using machine learning to identify suicide risk: a classification tree approach to prospectively identify adolescent suicide attempters",
journal="Archives of suicide research",
year="2019",
author="Hill, Ryan M. and Oosterhoff, Benjamin and Do, Calvin",
volume="ePub",
number="ePub",
pages="1-18",
abstract="OBJECTIVE: 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. <br><br>METHOD: Data were drawn from the National Longitudinal Study of Adolescent to Adult Health. Youth (n = 4,834, M<sub>age</sub>=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. <br><br>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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="1381-1118",
doi="10.1080/13811118.2019.1615018",
url="http://dx.doi.org/10.1080/13811118.2019.1615018"
}