ArticleDigital health
Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 3 syntheses or guidelines 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
- AI-Driven Mental Health Support for Caregivers of Individuals With Alzheimer Disease: Systematic Literature Review and Development of a Conceptual Framework.JMIR mental health · 2026Pooled it
- Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis.Clinical psychology review · 2025Pooled it
- Using machine learning to forecast symptom changes among subclinical depression patients receiving stepped care or usual care.Journal of affective disorders · 2023Trial
- 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
- Article
- Development of a baseline battery to optimize depression treatment assignment in primary care: Balancing breadth and brevity.Journal of affective disorders · 2026Article
- Predicting the path to guided ICBT success across referral pathway for moderate depression and anxiety disorders.Internet interventions · 2026Article
- Predicting individual differences in digital alcohol intervention effectiveness through multimodal data.NPJ digital medicine · 2026Article
- Machine Learning Model for Response to Internet-Delivered CBT vs Antidepressant Medication.JAMA network open · 2025Article
- Predicting Efficacy of Virtual Reality-Based Stabilization for Individuals With Posttraumatic Stress Symptoms: A Machine Learning Approach.Psychiatry investigation · 2025Article
- Observational
- Article
- Machine learning-based predictive modelling of mental health in Rwandan Youth.Scientific reports · 2025Article
- Article
- Predicting Satisfaction With Chat-Counseling at a 24/7 Chat Hotline for the Youth: Natural Language Processing Study.JMIR AI · 2025Article
- Article
- Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions.NPJ digital medicine · 2024Article
- Predicting anxiety treatment outcome in community mental health services using linked health administrative data.Scientific reports · 2024Article
- Predicting the Effectiveness of a Mindfulness Virtual Community Intervention for University Students: Machine Learning Model.Interactive journal of medical research · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePredicting the outcomes of individual participants for treatment interventions appears central to making mental healthcare more tailored and effective. However, little work has been done to investigate the performance of machine learning-based predictions within digital mental health interventions. Therefore, this study evaluates the performance of machine learning in predicting treatment response in a digital mental health intervention designed for treating depression and anxiety.
methodsSeveral algorithms were trained based on the data of 970 participants to predict a significant reduction in depression and anxiety symptoms using clinical and sociodemographic variables. As a random forest classifier performed best over cross-validation, it was used to predict the outcomes of 279 new participants.
resultsThe random forest achieved an accuracy of 0.71 for the test set (base rate: 0.67, area under curve (AUC): 0.60,
conclusionsThis study provides evidence that social-demographic and clinical variables can be used for machine learning to predict therapy outcomes within the context of a therapist-supported digital mental health intervention. Despite the overall moderate performance, this appears promising as these predictions can potentially improve the outcomes of non-responders by monitoring their progress or by offering alternative or additional treatment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.