ArticleScientific reports2020
Use of machine learning to classify adult ADHD and other conditions based on the Conners' Adult ADHD Rating Scales.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Individualized prediction models in ADHD: a systematic review and meta-regression.Molecular psychiatry · 2024Pooled it
- Early detection of adults ADHD using electronic health records: A machine learning study.European psychiatry : the journal of the Association of European Psychiatrists · 2026Article
- Validation of a criterion-based screening and triage pathway for adult ADHD: a prospective observational study of safety and operational efficiency.Frontiers in psychiatry · 2026Article
- Machine Learning for ADHD Diagnosis: Feature Selection from Parent Reports, Self-Reports and Neuropsychological Measures.Children (Basel, Switzerland) · 2025Article
- A multimodal approach for ADHD with coexisting ASD detection for children.Scientific reports · 2025Article
- Exploring voice as a digital phenotype in adults with ADHD.Scientific reports · 2025Article
- Interpretable machine learning approaches for children's ADHD detection using clinical assessment data: an online web application deployment.BMC psychiatry · 2025Article
- Objective Assessment of Attention Deficit Hyperactivity Disorder with QbMobile: A Smartphone Application for Clinical Use.Clinical practice and epidemiology in mental health : CP & EMH · 2025Article
- Machine learning on a smartphone-based CPT for ADHD prediction.Frontiers in psychiatry · 2025Article
- Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.Translational psychiatry · 2024Article
- Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study.JMIR formative research · 2024Article
- Main focus of parents of children with attention deficit hyperactivity disorder and the effectiveness of early clinical screening.World journal of clinical cases · 2024Article
- A novel framework to predict ADHD symptoms using irritability in adolescents and young adults with and without ADHD.Frontiers in psychiatry · 2024Article
- Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms.Translational psychiatry · 2023Review
- Prediction of Attention-Deficit/Hyperactivity Disorder Diagnosis Using Brief, Low-Cost Clinical Measures: A Competitive Model Evaluation.Clinical psychological science : a journal of the Association for Psychological Science · 2023Article
- Biological Predictors of Treatment Response in Adult Attention Deficit Hyperactivity Disorder (ADHD): A Systematic Review.Journal of personalized medicine · 2022Review
- Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records.Scientific reports · 2022Article
- Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test.Frontiers in psychology · 2022Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A reliable diagnosis of adult Attention Deficit/Hyperactivity Disorder (ADHD) is challenging as many of the symptoms of ADHD resemble symptoms of other disorders. ADHD is associated with gambling disorder and obesity, showing overlaps of about 20% with each diagnosis. It is important for clinical practice to differentiate between conditions displaying similar symptoms via established diagnostic instruments. Applying the LightGBM algorithm in machine learning, we were able to differentiate subjects with ADHD, obesity, problematic gambling, and a control group using all 26 items of the Conners' Adult ADHD Rating Scales (CAARS-S: S) with a global accuracy of .80; precision (positive predictive value) ranged between .78 (gambling) and .92 (obesity), recall (sensitivity) between .58 for obesity and .87 for ADHD. Models with the best 5 and best 10 items resulted in less satisfactory fits. The CAARS-S seems to be a promising instrument to be applied in clinical practice also for multiclassifying disorders displaying symptoms resembling ADHD.
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