Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study

Frank Iorfino, Nicholas Ho, Joanne S Carpenter, Shane P Cross, Tracey A Davenport, Daniel F Hermens, Hannah Yee, Alissa Nichles, Natalia Zmicerevska, Adam Guastella, Elizabeth Scott and Ian B Hickie

PLOS ONE, 2020, vol. 15, issue 12, 1-16

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. 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. 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. 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0243467

DOI: 10.1371/journal.pone.0243467

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