European child & adolescent psychiatry · 2026
From childhood to adulthood: an attempt to model long-term outcomes of ADHD using machine learning.
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van der Plas Noa, Bennis Frank, Franke Barbara, Hartman Catharina, Hoekstra Pieter, Nijmeijers Anna, Oblong Lennart, Rommelse Nanda, Sprooten Emma, Luman Marjolein, Oosterlaan Jaap, Noordermeer Siri
Аннотация
BACKGROUND: Childhood-onset attention-deficit/hyperactivity disorder (ADHD) persists in 28% to 58% of individuals, and is associated with adverse long-term outcomes, like additional psychiatric diagnoses and lower educational attainment. Knowing early life predictors for adverse long-term outcomes might help develop preventive measures, but too few have been identified using conventional statistical models. The current study investigated the potential of machine learning in identifying predictors for ADHD persistence and long-term functioning of adults with childhood-onset ADHD.
METHOD: A total of 133 adults (Mage = 29.53, SDage = 2.83, 75% males) from an 18-year prospective cohort study were assessed on ADHD persistence and long-term functioning. Machine learning models were developed using cross-validation, with and without additional resampling, and compared to a conventional logistic/linear regression model. With SHapley Additive exPlanations (SHAP) we explored model behavior. Childhood predictors encompassed ADHD symptoms and treatment, other psychopathology, cognition, somatic characteristics, ADHD polygenic risk score, and parental demographics and psychopathology.
RESULTS: Performance of all models for ADHD persistence and functioning (quality of life) was low. Best performing models included the logistic regression model with resampling for ADHD persistence (AUC'sall models = .52-.58; AUClogistic regression, resampling = .58), and the ridge regression model for long-term functioning (R2'sall models = -0.36-0.02; R2'sridge regression = .02, RMSE's all models = 2.07-2.42; RMSE'sridge regression = 2.07).
CONCLUSIONS: Findings highlight the complexity of predicting long-term outcomes of ADHD. Model improvement is crucial before models can inform clinical practice, e.g., by increasing sample size and investigating additional predictors.
METHOD: A total of 133 adults (Mage = 29.53, SDage = 2.83, 75% males) from an 18-year prospective cohort study were assessed on ADHD persistence and long-term functioning. Machine learning models were developed using cross-validation, with and without additional resampling, and compared to a conventional logistic/linear regression model. With SHapley Additive exPlanations (SHAP) we explored model behavior. Childhood predictors encompassed ADHD symptoms and treatment, other psychopathology, cognition, somatic characteristics, ADHD polygenic risk score, and parental demographics and psychopathology.
RESULTS: Performance of all models for ADHD persistence and functioning (quality of life) was low. Best performing models included the logistic regression model with resampling for ADHD persistence (AUC'sall models = .52-.58; AUClogistic regression, resampling = .58), and the ridge regression model for long-term functioning (R2'sall models = -0.36-0.02; R2'sridge regression = .02, RMSE's all models = 2.07-2.42; RMSE'sridge regression = 2.07).
CONCLUSIONS: Findings highlight the complexity of predicting long-term outcomes of ADHD. Model improvement is crucial before models can inform clinical practice, e.g., by increasing sample size and investigating additional predictors.