ArticlePsychological medicine2019
A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.
Article in Psychological medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 83 citations in OpenAlex.
- Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review.Frontiers in psychiatry · 2024Pooled it
- Computerized Psychological Interventions in Veterans and Service Members: Systematic Review of Randomized Controlled Trials.Journal of medical Internet research · 2022Pooled it
- Does outcome expectancy predict outcomes in online depression prevention? Secondary analysis of randomised-controlled trials.Health expectations : an international journal of public participation in health care and health policy · 2024Trial
- Predictors of adherence to an internet-based cognitive behavioral therapy program for individuals with chronic pain and comorbid psychological distress.BMC psychology · 2021Trial
- Using Artificial Intelligence to Predict Change in Depression and Anxiety Symptoms in a Digital Intervention: Evidence from a Transdiagnostic Randomized Controlled Trial.Psychiatry research · 2021Trial
- Factors underpinning international student mental well-being: a UK and China survey.BMJ mental health · 2026Article
- Deprexis for Veteran Depression: Open-Label Pilot Trial Examining Feasibility, Acceptability, and Preliminary Efficacy.JMIR formative research · 2026Article
- Functional, Contextual, and Interventional Drivers of Formal Coercion in Acute Mental Health Units: A Feature Analysis.International journal of mental health nursing · 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
- Ensemble learning approach for prediction of early complications after radiotherapy for head and neck cancer using CT and MRI radiomic features.Scientific reports · 2025Article
- Use machine learning to predict treatment outcome of early childhood caries.BMC oral health · 2025Article
- Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging.Scientific reports · 2025Article
- Machine learning models for predicting the risk of depressive symptoms in Chinese college students.Frontiers in psychiatry · 2025Article
- A prognostic index to predict symptom and functional outcomes of a coached, web-based intervention for trauma-exposed veterans.Psychological services · 2024Article
- The Evaluation of a Web-Based Intervention (Deprexis) to Decrease Depression and Restore Functioning in Veterans: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2024Article
- Methodological choices and clinical usefulness for machine learning predictions of outcome in Internet-based cognitive behavioural therapy.Communications medicine · 2024Article
- Complex Emotion Dynamics Contribute to the Prediction of Depression: A Machine Learning and Time Series Feature Extraction Approach.Affective science · 2024Article
- Which client with generalized anxiety disorder benefits from a mindfulness ecological momentary intervention versus a self-monitoring app? Developing a multivariable machine learning predictive model.Journal of anxiety disorders · 2024Article
- Predicting Individual Response to a Web-Based Positive Psychology Intervention: A Machine Learning Approach.The journal of positive psychology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 2 countries.
Funding
Abstract
backgroundSome Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment.
methodAn elastic net and random forest were trained to predict depression symptoms and related disability after an 8-week course of an Internet intervention, Deprexis, involving adults (N = 283) from across the USA. Candidate predictors included psychopathology, demographics, treatment expectancies, treatment usage, and environmental context obtained from population databases. Model performance was evaluated using predictive R2$\lpar R_{{\rm pred}}^2\rpar\comma $ the expected variance explained in a new sample, estimated by 10 repetitions of 10-fold cross-validation.
resultsAn ensemble model was created by averaging the predictions of the elastic net and random forest. Model performance was compared with a benchmark linear autoregressive model that predicted each outcome using only its baseline. The ensemble predicted more variance in post-treatment depression (8.0% gain, 95% CI 0.8-15; total $R_{{\rm pred}}^2 \; $= 0.25), disability (5.0% gain, 95% CI -0.3 to 10; total $R_{{\rm pred}}^2 \; $= 0.25), and well-being (11.6% gain, 95% CI 4.9-19; total $R_{{\rm pred}}^2 \; $= 0.29) than the benchmark model. Important predictors included comorbid psychopathology, particularly total psychopathology and dysthymia, low symptom-related disability, treatment credibility, lower access to therapists, and time spent using certain Deprexis modules.
conclusionA number of variables predict symptom improvement following an Internet intervention, but each of these variables makes relatively small contributions. Machine learning ensembles may be a promising statistical approach for identifying the cumulative contribution of many weak predictors to psychosocial depression treatment response.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.