Behaviour research and therapy · 2026
Predicting depression treatment outcomes for cognitive behavioural therapy using machine learning: A systematic review and meta-analysis.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод в очереди
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Lees Jennifer, Delgadillo Jaime
Аннотация
BACKGROUND: Cognitive behavioural therapy (CBT) is an empirically-supported treatment for depression, although some patients respond well and others do not. The use of machine learning (ML) could potentially help to predict which patients may benefit most from CBT.
OBJECTIVES: To synthesise the results of CBT studies applying ML to predict depression treatment outcomes.
METHODS: Systematic searches were conducted across three databases and eligible studies were assessed for risk of bias. ML methods and predictor variables were summarised using a narrative synthesis. Predictive performance indices were harmonized into a common effect size (r) and pooled using random-effects meta-analysis, separately for evaluations using [1] internal and [2] external cross-validation.
RESULTS: Twenty-four studies (n = 11,733) met eligibility criteria, of which only four (16.7%) were deemed at low risk of bias. The most commonly selected predictors were depression-related features (e.g., severity, symptom subtypes), functional/social impairment, sociodemographic characteristics (e.g., employment), comorbidity (mental/personality disorders) and adversity (current and past events). Studies with the most rigorous cross-validation methodology show replicated evidence that predictions from ML models generalise to external validation samples (in 5 out of 7), with a moderate effect size (r = 0.45; 95% CI: 0.26 to 0.63; p < .001).
LIMITATIONS: Reporting of technical details of ML model-training methods is generally sparse.
CONCLUSION: Replicated evidence indicates that ML methods can predict depression treatment outcomes, with adequate generalisability to new samples.
OBJECTIVES: To synthesise the results of CBT studies applying ML to predict depression treatment outcomes.
METHODS: Systematic searches were conducted across three databases and eligible studies were assessed for risk of bias. ML methods and predictor variables were summarised using a narrative synthesis. Predictive performance indices were harmonized into a common effect size (r) and pooled using random-effects meta-analysis, separately for evaluations using [1] internal and [2] external cross-validation.
RESULTS: Twenty-four studies (n = 11,733) met eligibility criteria, of which only four (16.7%) were deemed at low risk of bias. The most commonly selected predictors were depression-related features (e.g., severity, symptom subtypes), functional/social impairment, sociodemographic characteristics (e.g., employment), comorbidity (mental/personality disorders) and adversity (current and past events). Studies with the most rigorous cross-validation methodology show replicated evidence that predictions from ML models generalise to external validation samples (in 5 out of 7), with a moderate effect size (r = 0.45; 95% CI: 0.26 to 0.63; p < .001).
LIMITATIONS: Reporting of technical details of ML model-training methods is generally sparse.
CONCLUSION: Replicated evidence indicates that ML methods can predict depression treatment outcomes, with adequate generalisability to new samples.