Predicting psychopathology symptom trajectories using machine learning: a 33-year prospective study

While previous literature has established a link between psychopathology symptom trajectories and psychosocial risk, most studies have not extended into adulthood or incorporated comprehensive risk measures. We aimed to identify symptom trajectories for externalizing and internalizing symptoms over a 25-year span and predict them using machine learning.

We used data from a longitudinal birth cohort study (N = 317, 171 females). Psychopathology symptoms were measured between age 8 and 33. Symptom trajectories were identified using growth mixture models. An extensive battery of risk and protective measures over the lifespan (e.g., polygenic scores, psychosocial risk factors, temperament) was used to train machine learning-based classification models.

We identified three trajectories for both externalizing and internalizing symptoms (entropy > 0.8, likelihood ratio test p < .05): Low, increasing (late-adolescent emergence), and decreasing (childhood-limited) symptoms classes. Both genetic (e.g., polygenic scores for cognition and mental disorders) and environmental factors (e.g., adverse family characteristics, poor social functioning) predicted symptom trajectories. For externalizing symptoms trajectories, random forest achieved fair discriminatory power with a multiclass macro-average AUC of 0.77 and a multiclass micro-average of 0.88. For internalizing symptoms trajectories, logistic regression achieved modest discriminatory power with a multiclass macro-average AUC of 0.75 and a multiclass micro-average of 0.83.

Psychopathology risk trajectories vary across development and are influenced by diverse risk factors. Understanding these risk factors can support early identification of at-risk individuals and inform targeted prevention and intervention efforts.