ReviewBehavior therapy2020
Supervised Machine Learning: A Brief Primer.
Review in Behavior therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 272 papers, 3 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
272 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Optimal exercise interventions for enhancing cognitive function in older adults: a network meta-analysis.Frontiers in aging neuroscience · 2025Pooled it
- An in-depth exploration of machine learning methods for mental health state detection: a systematic review and analysis.Frontiers in digital health · 2025Pooled it
- The multiple uses of artificial intelligence in exercise programs: a narrative review.Frontiers in public health · 2025Pooled it
- Prediction of individual weight loss using supervised learning: findings from the CALERIEThe American journal of clinical nutrition · 2024Trial
- Individual Predictors of Response to A Behavioral Activation-Based Digital Smoking Cessation Intervention: A Machine Learning Approach.Substance use & misuse · 2024Trial
- Explainable machine learning integrating bioelectrical impedance for 6-month cardiovascular risk in peritoneal dialysis.Renal failure · 2026Article
- Esketamine multi-omic biomarker evaluation in major depressive disorder (EMBER-MDD): concept, objectives and methodologies of a non-clinical investigator-initiated study.European archives of psychiatry and clinical neuroscience · 2026Article
- Bridging data and theory: hybrid modelling for pharmaceutical crystallisation process development.Digital discovery · 2026Review
- Development of a Machine Learning Algorithm for Differential Diagnosis Between Primary Immune Thrombocytopenia and Connective Tissue Disease-Related Thrombocytopenia in Pediatric Patients.Journal of clinical laboratory analysis · 2026Article
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Machine learning classification of dilated cardiomyopathy severity in commercial broiler chickens.Poultry science · 2026Article
- Functional and Structural Outcomes of Photodynamic Therapy (PDT) With or Without Eplerenone for Central Serous Chorioretinopathy.Journal of clinical medicine · 2026Article
- Biomarker-based machine learning for malignant transformation in oral potentially malignant disorders: a scoping review.BMC oral health · 2026Article
- CLIMBS: Assessing Carbohydrate-Protein Interactions through a Graph Neural Network Classifier Using Synthetic Negative Data.Journal of chemical information and modeling · 2026Article
- Radiomics in thyroid nodule assessment.BMC medical imaging · 2026Review
- Interpretable machine learning models based on CMR radiomics for predicting left ventricular diastolic dysfunction in patients with metabolic-associated steatotic liver disease and type 2 diabetes mellitus.Journal of diabetes investigation · 2026Article
- Intradialytic hypotension and hemodynamic phenotypes in children following continuous renal replacement therapy initiation.Pediatric research · 2026Observational
- Natural Language Processing Framework in Early Detection of Amyloidosis: The ALARM Study.JACC. Advances · 2026Article
- Multidomain correlates of burnout: A population-based study using supervised machine learning.Social psychiatry and psychiatric epidemiology · 2026Article
- Validation of an Automated AI Algorithm for the Quantification of Major OCT Parameters in Retinal Vein Occlusion-Related Macular Edema.Journal of clinical medicine · 2026Article
212 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Machine learning is increasingly used in mental health research and has the potential to advance our understanding of how to characterize, predict, and treat mental disorders and associated adverse health outcomes (e.g., suicidal behavior). Machine learning offers new tools to overcome challenges for which traditional statistical methods are not well-suited. This paper provides an overview of machine learning with a specific focus on supervised learning (i.e., methods that are designed to predict or classify an outcome of interest). Several common supervised learning methods are described, along with applied examples from the published literature. We also provide an overview of supervised learning model building, validation, and performance evaluation. Finally, challenges in creating robust and generalizable machine learning algorithms are discussed.
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.