Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning.
Journal:
Journal of child psychology and psychiatry, and allied disciplines
PMID:
29709069
Abstract
BACKGROUND: Adolescents have high rates of nonfatal suicide attempts, but clinically practical risk prediction remains a challenge. Screening can be time consuming to implement at scale, if it is done at all. Computational algorithms may predict suicide risk using only routinely collected clinical data. We used a machine learning approach validated on longitudinal clinical data in adults to address this challenge in adolescents.