Trial reportPloS one2020
Predicting mental health problems in adolescence using machine learning techniques.
Trial report in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 4 of them syntheses that pooled it.
What it found
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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
52 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Integrating Neuroimaging and Machine Learning to Predict Mental Disorder Outcomes: A Systematic Review.Advances in experimental medicine and biology · 2026Pooled it
- The application of artificial intelligence in the field of mental health: a systematic review.BMC psychiatry · 2025Pooled it
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 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
- Predicting psychopathology symptom trajectories using machine learning: a 33-year prospective study.Journal of child psychology and psychiatry, and allied disciplines · 2026Article
- Leveraging Machine Learning to Uncover Ethnic-Specific Predictors of Maternal Postpartum Depression.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Predicting 7-year-olds mental health in the perinatal period: Development and internal validation of a multivariable model using the prospective ALSPAC cohort.JCPP advances · 2026Article
- Presymptomatic Screening for Risks to Children's Mental Health : Ethical Considerations from a European Focus Group Study with Mental Health Professionals.Journal of bioethical inquiry · 2026Article
- Article
- A method for predicting student psychological health based on behavioral time series analysis.Frontiers in psychiatry · 2026Article
- Early warning indicator system for adolescent student mental health: theoretical construction of the relationship-belief-process-state model.Frontiers in psychology · 2026Review
- Predicting 3-year depressive symptoms among middle-aged and older adults in rural China using random forest: insights from the China health and retirement longitudinal study.BMC psychology · 2025Article
- Apply Machine Learning to Predict Risk for Adolescent Depression in a Cohort of Kenyan Adolescents.Healthcare (Basel, Switzerland) · 2025Article
- Large language models predict cognition and education close to or better than genomics or expert assessment.Communications psychology · 2025Article
- Assessing a prediction model for depression risk using an early adolescent sample with self-reported depression.JCPP advances · 2025Article
- Peer Relationships Are a Direct Cause of the Adolescent Mental Health Crisis: Interpretable Machine Learning Analysis of 2 Large Cohort Studies.JMIR public health and surveillance · 2025Article
- Suicide risk prediction for Korean adolescents based on machine learning.Scientific reports · 2025Article
- Machine learning for prediction of childhood mental health problems in social care.BJPsych open · 2025Article
- A feasibility study of a preventative, transdiagnostic intervention for mental health problems in adolescence: building resilience through socioemotional training (ReSET).Child and adolescent psychiatry and mental health · 2025Article
- A Machine-Learning-Based Approach to Informing Student Admission Decisions.Behavioral sciences (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting which children will go on to develop mental health symptoms as adolescents is critical for early intervention and preventing future, severe negative outcomes. Although many aspects of a child's life, personality, and symptoms have been flagged as indicators, there is currently no model created to screen the general population for the risk of developing mental health problems. Additionally, the advent of machine learning techniques represents an exciting way to potentially improve upon the standard prediction modelling technique, logistic regression. Therefore, we aimed to I.) develop a model that can predict mental health problems in mid-adolescence II.) investigate if machine learning techniques (random forest, support vector machines, neural network, and XGBoost) will outperform logistic regression.
methodsIn 7,638 twins from the Child and Adolescent Twin Study in Sweden we used 474 predictors derived from parental report and register data. The outcome, mental health problems, was determined by the Strengths and Difficulties Questionnaire. Model performance was determined by the area under the receiver operating characteristic curve (AUC).
resultsAlthough model performance varied somewhat, the confidence interval overlapped for each model indicating non-significant superiority for the random forest model (AUC = 0.739, 95% CI 0.708-0.769), followed closely by support vector machines (AUC = 0.735, 95% CI 0.707-0.764).
conclusionUltimately, our top performing model would not be suitable for clinical use, however it lays important groundwork for future models seeking to predict general mental health outcomes. Future studies should make use of parent-rated assessments when possible. Additionally, it may not be necessary for similar studies to forgo logistic regression in favor of other more complex methods.
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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.