ArticleTranslational psychiatry2022
Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data.
Article in Translational psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis.Clinical psychology review · 2025Pooled it
- [Meta analysis of the prevalence of depressive symptoms among children and adolescents in China from 2015 to 2024].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2025Pooled it
- Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review.Frontiers in psychiatry · 2024Pooled it
- Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury.BMC psychiatry · 2024Trial
- Advancing equity in routine-care internet-delivered cognitive behaviour therapy: Representativeness, outcomes, engagement, and satisfaction.Internet interventions · 2026Article
- 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
- A clinically actionable framework for personalizing iCBT to improve depression outcomes.NPJ digital medicine · 2026Review
- Article
- Prediction of 12-Week Remission in Patients With Depressive Disorder Using Reasoning-Based Large Language Models: Model Development and Validation Study.JMIR mental health · 2026Article
- Multimodal AI-based systems in major depressive disorder: a review of clinical and translational applications.Frontiers in digital health · 2026Review
- Application of internet-based cognitive behavioral therapy for negative emotions in pregnant women: a narrative review.Frontiers in psychiatry · 2026Review
- Machine Learning Model for Response to Internet-Delivered CBT vs Antidepressant Medication.JAMA network open · 2025Article
- Association Between Polygenic Risk and Symptom Severity Change After Cognitive Behavioral Therapy for Obsessive-Compulsive Disorder.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2025Article
- Polygenic scores and symptom severity change after internet-delivered cognitive behaviour therapy for depression and anxiety.Discover mental health · 2025Article
- Research Review: A review of the past decade of family and genomic studies on adolescent mental health.Journal of child psychology and psychiatry, and allied disciplines · 2025Review
- Clinical, genetic, and sociodemographic predictors of symptom severity after internet-delivered cognitive behavioural therapy for depression and anxiety.BMC psychiatry · 2025Article
- Predicting Therapy Outcomes in Patients With Stress-Related Disorders: Protocol for a Predictive Modeling Study.JMIR research protocols · 2025Article
- Prediction of individual patient outcomes to psychotherapy vs medication for major depression.Npj mental health research · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
This study applied supervised machine learning with multi-modal data to predict remission of major depressive disorder (MDD) after psychotherapy. Genotyped adult patients (n = 894, 65.5% women, age 18-75 years) diagnosed with mild-to-moderate MDD and treated with guided Internet-based Cognitive Behaviour Therapy (ICBT) at the Internet Psychiatry Clinic in Stockholm were included (2008-2016). Predictor types were demographic, clinical, process (e.g., time to complete online questionnaires), and genetic (polygenic risk scores). Outcome was remission status post ICBT (cut-off ≤10 on MADRS-S). Data were split into train (60%) and validation (40%) given ICBT start date. Predictor selection employed human expertise followed by recursive feature elimination. Model derivation was internally validated through cross-validation. The final random forest model was externally validated against a (i) null, (ii) logit, (iii) XGBoost, and (iv) blended meta-ensemble model on the hold-out validation set. Feature selection retained 45 predictors representing all four predictor types. With unseen validation data, the final random forest model proved reasonably accurate at classifying post ICBT remission (Accuracy 0.656 [0.604, 0.705], P vs null model = 0.004; AUC 0.687 [0.631, 0.743]), slightly better vs logit (bootstrap D = 1.730, P = 0.084) but not vs XGBoost (D = 0.463, P = 0.643). Transparency analysis showed model usage of all predictor types at both the group and individual patient level. A new, multi-modal classifier for predicting MDD remission status after ICBT treatment in routine psychiatric care was derived and empirically validated. The multi-modal approach to predicting remission may inform tailored treatment, and deserves further investigation to attain clinical usefulness.
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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.