ArticleScientific reports2022
Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01649232 (Implications of Electrophysiological ADHD Endophenotypes to Predict Response to Transcranial Direct-Current Stimulation), which is not on this map. Cited by 18 papers, 1 of them a synthesis 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.
Implications of Electrophysiological ADHD Endophenotypes to Predict Response to Transcranial Direct-Current Stimulation
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Individualized prediction models in ADHD: a systematic review and meta-regression.Molecular psychiatry · 2024Pooled it
- Virtual reality (bio-)markers differentiate ADHD from depression and controls.European archives of psychiatry and clinical neuroscience · 2026Article
- Artificial intelligence for predicting depression anxiety and stress using psychometric data.Scientific reports · 2025Article
- Machine Learning for ADHD Diagnosis: Feature Selection from Parent Reports, Self-Reports and Neuropsychological Measures.Children (Basel, Switzerland) · 2025Article
- Explainable AI for Depression Detection and Severity Classification From Activity Data: Development and Evaluation Study of an Interpretable Framework.JMIR mental health · 2025Article
- Toward a fair, gender-debiased classifier for the diagnosis of attention deficit/hyperactivity disorder- a Machine-Learning based classification study.BMC medical informatics and decision making · 2025Article
- Investigating psychotherapists' attitudes towards artificial intelligence in psychotherapy.BMC psychology · 2025Article
- Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction.BMC psychiatry · 2025Article
- Machine learning on a smartphone-based CPT for ADHD prediction.Frontiers in psychiatry · 2025Article
- Unlocking the potential of wearable technology: Fitbit-derived measures for predicting ADHD in adolescents.Frontiers in child and adolescent psychiatry · 2025Article
- Detecting ADHD through natural language processing and stylometric analysis of adolescent narratives.Frontiers in child and adolescent psychiatry · 2025Article
- Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.Experimental biology and medicine (Maywood, N.J.) · 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
- Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.Journal of neurodevelopmental disorders · 2024Review
- Trajectory Analysis for Identifying Classes of Attention Deficit Hyperactivity Disorder (ADHD) in Children of the United States.Clinical practice and epidemiology in mental health : CP & EMH · 2024Article
- Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders.Frontiers in neuroscience · 2024Article
- A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis.Healthcare (Basel, Switzerland) · 2023Review
- Overcoming the phenomenological Perpetuum mobile in clinical cognitive neuroscience for the benefit of replicability in research and the societal view on mental disorders.Frontiers in human neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diagnostic process of attention deficit hyperactivity disorder (ADHD) is complex and relies on criteria sensitive to subjective biases. This may cause significant delays in appropriate treatment initiation. An automated analysis relying on subjective and objective measures might not only simplify the diagnostic process and reduce the time to diagnosis, but also improve reproducibility. While recent machine learning studies have succeeded at distinguishing ADHD from healthy controls, the clinical process requires differentiating among other or multiple psychiatric conditions. We trained a linear support vector machine (SVM) classifier to detect participants with ADHD in a population showing a broad spectrum of psychiatric conditions using anonymized data from clinical records (N = 299 participants). We differentiated children and adolescents with ADHD from those not having the condition with an accuracy of 66.1%. SVM using single features showed slight differences between features and overlapping standard deviations of the achieved accuracies. An automated feature selection achieved the best performance using a combination 19 features. Real-world clinical data from medical records can be used to automatically identify individuals with ADHD among help-seeking individuals using machine learning. The relevant diagnostic information can be reduced using an automated feature selection without loss of performance. A broad combination of symptoms across different domains, rather than specific domains, seems to indicate an ADHD diagnosis.
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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.