Predicting mental health trajectories after potentially traumatic events: a machine learning approach.

UNLABELLED: This study aimed to investigate the trajectories of internalizing and externalizing problems following childhood potentially traumatic events (PTEs) and analyse a comprehensive set of baseline variables (PTEs, individual, environmental) to elucidate their predictive role as contributors to different mental health trajectories. The sample consisted of 4,141 participants (M = 9.48, SD = 0.51 years at baseline; 48.7% girls; 72.1% White) from the Adolescent Brain Cognitive Development study who had experienced at least one PTE. Participants’ mental health problems were assessed using the Brief Problem Monitor self-report form. Latent Growth Mixture Modelling was used to identify trajectories of youth´s internalizing and externalizing problems across the six assessments. Machine learning was utilized to investigate 37 predictors of different trajectories. Three distinct trajectories were identified for both internalizing and externalizing problems: “Resilient”, “Mild” and “Moderate chronic”. Predictors of the “Moderate chronic” versus “Resilient” trajectories were identified using machine learning. The three most important predictors of the internalizing problems trajectory were: behavioural inhibition, caregiver internalizing problems, and female gender, whereas predictors of the externalizing problems trajectory were family conflicts, screentime, and a low level of prosocial behaviour. The findings can help characterize individual variation in mental health trajectories following childhood PTEs and provide potential targets for intervention to foster mental health.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00787-026-03022-6.