ArticleCommunications medicine2024
Methodological choices and clinical usefulness for machine learning predictions of outcome in Internet-based cognitive behavioural therapy.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Predicting Treatment Response in Patients With Anxiety Disorders After Cognitive Behavioral Therapy: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study.Journal of medical Internet research · 2026Article
- Predicting individual differences in digital alcohol intervention effectiveness through multimodal data.NPJ digital medicine · 2026Article
- Latent trait or sum score: addressing measurement challenges in the prediction of self-rated symptom outcomes in psychological treatment.Frontiers in psychology · 2026Article
- Natural language processing models for predicting treatment outcomes in internet-delivered cognitive behavioral therapy.Internet interventions · 2025Article
- Generative AI-assisted clinical interviewing of mental health.Scientific reports · 2025Article
- Integrative analysis of psychosocial, chronotype, and environmental predictors of aggressive behavior in Adolescents: Insights from machine learning.Journal of psychiatric research · 2025Article
- Effects of intervention design on engagement and outcomes in digital self-help for insomnia - factorial RCT.NPJ digital medicine · 2025Article
- The promise and challenges of computer mouse trajectories in DMHIs - A feasibility study on pre-treatment dropout predictions.Internet interventions · 2025Article
- Study protocol for a triple-blind randomised controlled trial evaluating a machine learning-based predictive clinical decision support tool for internet-delivered cognitive behaviour therapy (ICBT) for depression and anxiety.Internet interventions · 2025Article
- Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions.NPJ digital medicine · 2024Article
- Making the most out of timeseries symptom data: A machine learning study on symptom predictions of internet-based CBT.Internet interventions · 2024Article
- Methodological choices and clinical usefulness for machine learning predictions of outcome in Internet-based cognitive behavioural therapy.Communications medicine · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhile psychological treatments are effective, a substantial portion of patients do not benefit enough. Early identification of those may allow for adaptive treatment strategies and improved outcomes. We aimed to evaluate the clinical usefulness of machine-learning (ML) models predicting outcomes in Internet-based Cognitive Behavioural Therapy, to compare ML-related methodological choices, and guide future use of these.
methodsEighty main models were compared. Baseline variables, weekly symptoms, and treatment activity were used to predict treatment outcomes in a dataset of 6695 patients from regular care.
resultsWe show that the best models use handpicked predictors and impute missing data. No ML algorithm shows clear superiority. They have a mean balanced accuracy of 78.1% at treatment week four, closely matched by regression (77.8%).
conclusionsML surpasses the benchmark for clinical usefulness (67%). Advanced and simple models perform equally, indicating a need for more data or smarter methodological designs to confirm advantages of ML.
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Registered trials
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