ArticleMolecular psychiatry2023
Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach.
Article in Molecular psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- A Systematic Review of Wearable Sensors in Rett Syndrome-What Physiological Markers Are Informative for Monitoring Disease States?Sensors (Basel, Switzerland) · 2025Pooled it
- Individualized prediction models in ADHD: a systematic review and meta-regression.Molecular psychiatry · 2024Pooled it
- Integrating Genetic, Environmental, Cognitive, and Temperament Data for ADHD Prediction in Explainable Deep Learning Models.medRxiv : the preprint server for health sciences · 2026Article
- Early attention deficit hyperactivity disorder prediction from longitudinal electronic health records.Nature. Mental health · 2026Article
- Attention Deficit Hyperactivity Disorder (ADHD) and Self-harm in Chinese Children and Adolescents: A Mediation by Resilience.Journal of autism and developmental disorders · 2026Article
- Investigation of quantitative synthetic MRI in the evaluation of attention-deficit/hyperactivity disorder.Quantitative imaging in medicine and surgery · 2026Article
- A Random Forest-Based Risk Prediction Model for Non-Response to Methylphenidate in Children with Attention Deficit Hyperactivity Disorder.Neuropsychiatric disease and treatment · 2026Article
- Digital health technologies for adults with ADHD: a scoping review.Frontiers in digital health · 2026Review
- Predicting ADHD in Children and Adolescents With Artificial Intelligence: A Scoping Review of Common Models.Health science reports · 2025Article
- Machine Learning for ADHD Diagnosis: Feature Selection from Parent Reports, Self-Reports and Neuropsychological Measures.Children (Basel, Switzerland) · 2025Article
- Practical AI application in psychiatry: historical review and future directions.Molecular psychiatry · 2025Review
- RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data.Patterns (New York, N.Y.) · 2025Article
- Transformer-based structural connectivity networks for ADHD-related connectivity alterations.Communications medicine · 2025Article
- Attention-Deficit/Hyperactivity Disorder Symptoms Are Common and Associated with Worse Glycemic Control in Adults with Type 1 Diabetes.Journal of clinical medicine · 2025Article
- Predicting the onset of mental health problems in adolescents.Psychological medicine · 2025Article
- EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism.Brain sciences · 2025Article
- Using machine and deep learning to predict short-term complications following trigger digit release surgery.Journal of hand and microsurgery · 2025Article
- Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.Frontiers in psychiatry · 2025Article
- Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.Journal of neurodevelopmental disorders · 2024Review
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 at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder with a high degree of psychiatric and physical comorbidity, which complicates its diagnosis in childhood and adolescence. We analyzed registry data from 238,696 persons born and living in Sweden between 1995 and 1999. Several machine learning techniques were used to assess the ability of registry data to inform the diagnosis of ADHD in childhood and adolescence: logistic regression, random Forest, gradient boosting, XGBoost, penalized logistic regression, deep neural network (DNN), and ensemble models. The best fitting model was the DNN, achieving an area under the receiver operating characteristic curve of 0.75, 95% CI (0.74-0.76) and balanced accuracy of 0.69. At the 0.45 probability threshold, sensitivity was 71.66% and specificity was 65.0%. There was an overall agreement in the feature importance among all models (τ > .5). The top 5 features contributing to classification were having a parent with criminal convictions, male sex, having a relative with ADHD, number of academic subjects failed, and speech/learning disabilities. A DNN model predicting childhood and adolescent ADHD trained exclusively on Swedish register data achieved good discrimination. If replicated and validated in an external sample, and proven to be cost-effective, this model could be used to alert clinicians to individuals who ought to be screened for ADHD and to aid clinicians' decision-making with the goal of decreasing misdiagnoses. Further research is needed to validate results in different populations and to incorporate new predictors.
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.