Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study.
<h4>Background</h4>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 ter...
Main Authors: | 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 |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2020-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0243467 |
Similar Items
-
A Digital Platform Designed for Youth Mental Health Services to Deliver Personalized and Measurement-Based Care
by: Frank Iorfino, et al.
Published: (2019-08-01) -
Exploring associations between early substance use and longitudinal socio-occupational functioning in young people engaged in a mental health service.
by: Jacob J Crouse, et al.
Published: (2019-01-01) -
Cohort profile: the Brain and Mind Centre Optymise cohort: tracking multidimensional outcomes in young people presenting for mental healthcare
by: Daniel F Hermens, et al.
Published: (2020-03-01) -
Right Care, First Time: Developing a Theory-Based Automated Protocol to Help Clinically Stage Young People Based on Severity and Persistence of Mental Illness
by: Frank Iorfino, et al.
Published: (2021-08-01) -
Reducing youth suicide: systems modelling and simulation to guide targeted investments across the determinants
by: Jo-An Occhipinti, et al.
Published: (2021-03-01)