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

Author

Listed:
  • 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
  • Ian B Hickie

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.

Suggested Citation

  • 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 & Ian B , 2020. "Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-16, December.
  • Handle: RePEc:plo:pone00:0243467
    DOI: 10.1371/journal.pone.0243467
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    References listed on IDEAS

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    1. Matthew Large & Muthusamy Kaneson & Nicholas Myles & Hannah Myles & Pramudie Gunaratne & Christopher Ryan, 2016. "Meta-Analysis of Longitudinal Cohort Studies of Suicide Risk Assessment among Psychiatric Patients: Heterogeneity in Results and Lack of Improvement over Time," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-17, June.
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