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<p>Machine Learning (ML) is a promising approach for predicting outcomes of youth anxiety treatments. To this end, data from nine randomized controlled trials of youth anxiety treatments were concatenated into a dataset (<i>N</i> = 1362; <i>M</i><sub><i>age</i></sub> = 10.59, <i>SD</i><sub><i>age</i></sub> = 2.47; 48.9% female; 71.9% White, 5.9% Black, Other, 5.9%; 10.8% Hispanic) and ML algorithms were used to predict outcomes. Models were then applied on an external validation sample in a research clinic (<i>N</i> = 50; <i>M</i><sub><i>age</i></sub> = 12.04, <i>SD</i><sub><i>age</i></sub> = 3.22; 56% female; 76% Caucasian, 10% Black, 6% Asian, 2% Other; 6% Hispanic). To examine predictive features by treatment type, Lasso Regression models were built separately for youth who completed individual cognitive behavioral therapy (CBT), family CBT (FCBT), sertraline alone (SRT), and combination of SRT and CBT (COMB). Automatic relevance determination (ARD) emerged as the best performing model in the concatenated (<i>RMSE</i> = 1.84, <i>R</i><sup><i>2</i></sup> = 0.28) and external validation datasets (<i>RMSE</i> = 1.87, <i>R</i><sup><i>2</i></sup> = 0.11). Predictive features of poorer outcomes were primarily indicators of symptom severity and trial effects, although predictors varied within treatments (e.g., caregiver psychopathology was predictive for FCBT; depressive symptoms were predictive for COMB). Implications for use of ML to predict outcomes are discussed.</p>

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Using Machine Learning to Predict Treatment Outcome in a Concatenated Dataset of Youth Anxiety Treatments

  • 期刊论文

  • 发表日期：

    2025年7月3日

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  • Lesley A. Norris,
  • Marija Stanojevic,
  • Laura C. Skriner,
  • Brian C. Chu,
  • Marianne Aalberg,
  • Wendy K. Silverman,
  • Denise Bodden,
  • John C. Piacentini,
  • Zoran Obradovic,
  • Philip C. Kendall

摘要

Machine Learning (ML) is a promising approach for predicting outcomes of youth anxiety treatments. To this end, data from nine randomized controlled trials of youth anxiety treatments were concatenated into a dataset (N = 1362; Mage = 10.59, SDage = 2.47; 48.9% female; 71.9% White, 5.9% Black, Other, 5.9%; 10.8% Hispanic) and ML algorithms were used to predict outcomes. Models were then applied on an external validation sample in a research clinic (N = 50; Mage = 12.04, SDage = 3.22; 56% female; 76% Caucasian, 10% Black, 6% Asian, 2% Other; 6% Hispanic). To examine predictive features by treatment type, Lasso Regression models were built separately for youth who completed individual cognitive behavioral therapy (CBT), family CBT (FCBT), sertraline alone (SRT), and combination of SRT and CBT (COMB). Automatic relevance determination (ARD) emerged as the best performing model in the concatenated (RMSE = 1.84, R2 = 0.28) and external validation datasets (RMSE = 1.87, R2 = 0.11). Predictive features of poorer outcomes were primarily indicators of symptom severity and trial effects, although predictors varied within treatments (e.g., caregiver psychopathology was predictive for FCBT; depressive symptoms were predictive for COMB). Implications for use of ML to predict outcomes are discussed.