TY  - JOUR
PY  - 2020//
TI  - Predicting self-harm within six months after initial presentation to youth mental health services: a machine learning study
JO  - PLoS one
A1  - Iorfino, Frank
A1  - Ho, Nicholas
A1  - Carpenter, Joanne S.
A1  - Cross, Shane P.
A1  - Davenport, Tracey A.
A1  - Hermens, Daniel F.
A1  - Yee, Hannah
A1  - Nichles, Alissa
A1  - Zmicerevska, Natalia
A1  - Guastella, Adam
A1  - Scott, Elizabeth
A1  - Hickie, Ian B.
SP  - e0243467
EP  - e0243467
VL  - 15
IS  - 12
N2  - 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>
LA  - en
SN  - 1932-6203
UR  - http://dx.doi.org/10.1371/journal.pone.0243467
ID  - ref1
ER  -