Modeling trajectories of mental health: challenges and opportunities
This work addresses the problem of early mental illness prediction for children and adolescents, but it is incremental as it compares existing methods without achieving clinical accuracy.
The study tackled the challenge of predicting childhood mental health trajectories using longitudinal data, finding that latent class mixture models matched the accuracy of Gaussian process models but were significantly faster, though neither model was accurate enough for immediate clinical use.
More than two thirds of mental health problems have their onset during childhood or adolescence. Identifying children at risk for mental illness later in life and predicting the type of illness is not easy. We set out to develop a platform to define subtypes of childhood social-emotional development using longitudinal, multifactorial trait-based measures. Subtypes discovered through this study could ultimately advance psychiatric knowledge of the early behavioural signs of mental illness. To this extent we have examined two types of models: latent class mixture models and GP-based models. Our findings indicate that while GP models come close in accuracy of predicting future trajectories, LCMMs predict the trajectories as well in a fraction of the time. Unfortunately, neither of the models are currently accurate enough to lead to immediate clinical impact. The available data related to the development of childhood mental health is often sparse with only a few time points measured and require novel methods with improved efficiency and accuracy.