@article{ref1,
title="Predicting self-harm within six months after initial presentation to youth mental health services: a machine learning study",
journal="PLoS one",
year="2020",
author="Iorfino, Frank and Ho, Nicholas and Carpenter, Joanne S. and Cross, Shane P. and Davenport, Tracey A. and Hermens, Daniel F. and Yee, Hannah and Nichles, Alissa and Zmicerevska, Natalia and Guastella, Adam and Scott, Elizabeth and Hickie, Ian B.",
volume="15",
number="12",
pages="e0243467-e0243467",
abstract="BACKGROUND: A priority for health services is to reduce self-harm in young people. Predicting self-harm is challenging due to their rarity and complexity, however this  does not preclude the utility of prediction models to improve decision-making  regarding a service response in terms of more detailed assessments and/or  intervention. The aim of this study was to predict self-harm within six-months after  initial presentation. <br><br>METHOD: The study included 1962 young people (12-30 years)  presenting to youth mental health services in Australia. Six machine learning  algorithms were trained and tested with ten repeats of ten-fold cross-validation. The net benefit of these models were evaluated using decision curve analysis. <br><br>RESULTS: Out of 1962 young people, 320 (16%) engaged in self-harm in the six months  after first assessment and 1642 (84%) did not. The top 25% of young people as ranked  by mean predicted probability accounted for 51.6% - 56.2% of all who engaged in  self-harm. By the top 50%, this increased to 82.1%-84.4%. Models demonstrated fair  overall prediction (AUROCs; 0.744-0.755) and calibration which indicates that  predicted probabilities were close to the true probabilities (brier scores;  0.185-0.196). The net benefit of these models were positive and superior to the  'treat everyone' strategy. The strongest predictors were (in ranked order); a  history of self-harm, age, social and occupational functioning, sex, bipolar  disorder, psychosis-like experiences, treatment with antipsychotics, and a history  of suicide ideation. <br><br>CONCLUSION: Prediction models for self-harm may have utility to  identify a large sub population who would benefit from further assessment and  targeted (low intensity) interventions. Such models could enhance health service  approaches to identify and reduce self-harm, a considerable source of distress,  morbidity, ongoing health care utilisation and mortality.<p /> <p>Language: en</p>",
language="en",
issn="1932-6203",
doi="10.1371/journal.pone.0243467",
url="http://dx.doi.org/10.1371/journal.pone.0243467"
}