@article{ref1,
title="Machine learning-based prediction for self-harm and suicide attempts in adolescents",
journal="Psychiatry research",
year="2023",
author="Su, Raymond and John, James Rufus and Lin, Ping-I.",
volume="328",
number="",
pages="e115446-e115446",
abstract="This study aimed to use machine learning (ML) models to predict the risk of self-harm and suicide attempts in adolescents. We conducted secondary analysis of cross-sectional data from the Longitudinal Study of Australian Children dataset. Several key variables at the age of 14-15 years were used to predict self-harm or suicide attempt at 16-17 years. Random forest classification models were used to select the optimal subset of predictors and subsequently make predictions. Among 2809 participants, 296 (10.54%) reported an act of self-harm and 145 (5.16%) reported attempting suicide at least once in the past 12 months. The area under the receiver operating curve was fair for self-harm (0.7397) and suicide attempt (0.7220), which outperformed the prediction strategy solely based on prior suicide or self-harm attempt (AUC: 0.6). The most important factors identified were similar, and included depressed feelings, strengths and difficulties questionnaire scores, perceptions of self, and school- and parent-related factors. The random forest classification algorithm, an ML technique, can effectively select the optimal subset of predictors from hundreds of variables to forecast the risks of suicide and self-harm among adolescents. Further research is needed to validate the utility and scalability of ML techniques in mental health research.<p /> <p>Language: en</p>",
language="en",
issn="0165-1781",
doi="10.1016/j.psychres.2023.115446",
url="http://dx.doi.org/10.1016/j.psychres.2023.115446"
}