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June 17, 2026Journal of Child Psychology and PsychiatryOpen Access

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

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Authors

SSSeda SacuHeidelberg UniversityFSF StreitHeidelberg UniversitySWS de WittHeidelberg University

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Implication

Randomized trial identifies symptom trajectories in a birth cohort, suggesting multiple risk influences.

Key Points

  • The aim is to identify and predict symptom trajectories of psychopathology from childhood to adulthood using machine learning.
  • Longitudinal birth cohort study with 317 participants, assessed between ages 8 and 33.
  • Growth mixture models were used to identify symptom trajectories for externalizing and internalizing symptoms.
  • Machine learning classification models were trained using both genetic and environmental risk measures.
  • Three symptom trajectories identified: low, increasing, and decreasing for both externalizing and internalizing symptoms (entropy > 0.8, p < .05).
  • Random forest model achieved a multiclass macro-average AUC of 0.77 for externalizing symptoms.
  • Logistic regression model achieved a multiclass macro-average AUC of 0.75 for internalizing symptoms.

Cite This Study

Sacu et al. (2026) studied this question.

synapsesocial.com/papers/6a323c94d50b63ecad206c0dhttps://doi.org/10.1111/jcpp.70180
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