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

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 s

SUBMITTER: Iorfino F 

PROVIDER: S-EPMC7775066 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature