Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury.

ABSTRACT:

Background

Nonsuicidal self-injury is a common health problem in adolescents and associated with future suicidal behavior. Predicting who will benefit from treatment is an urgent and a critical first step towards personalized treatment approaches. Machine-learning algorithms have been proposed as techniques that might outperform clinicians' judgment. The aim of this study was to explore clinician predictions of which adolescents would abstain from nonsuicidal self-injury after treatment as well as how these predictions match machine-learning algorithm predictions.

Methods

Data from a recent trial evaluating an internet-delivered emotion regulation therapy for adolescents with nonsuicidal self-injury was used. Clinician predictions of which patients would abstain from nonsuic

SUBMITTER: Ponten M 

PROVIDER: S-EPMC11653784 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature