ArticlePloS one2019
Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in ADHD: a global perspective on research hotspots, trends and clinical applications.Frontiers in human neuroscience · 2025Pooled it
- Individualized prediction models in ADHD: a systematic review and meta-regression.Molecular psychiatry · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- A simulation study of a long-term neurodevelopmental care pathway for children with congenital heart disease.Pediatric research · 2026Article
- Predicting ADHD in Children and Adolescents With Artificial Intelligence: A Scoping Review of Common Models.Health science reports · 2025Article
- Advocating for trust in and trustworthy AI to transform evidence synthesis.Environmental evidence · 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
- Multimodality model investigating the impact of brain atlases, connectivity measures, and dimensionality reduction techniques on Attention Deficit Hyperactivity Disorder diagnosis using resting state functional connectivity.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification.Frontiers in neuroscience · 2023Article
- Article
- Neuroimaging in attention-deficit/hyperactivity disorder.Current opinion in psychiatry · 2021Review
- Predicting individual clinical trajectories of depression with generative embedding.NeuroImage. Clinical · 2020Article
- Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey.Frontiers in neuroinformatics · 2020Review
- At the Crossroads Between Psychiatry and Machine Learning: Insights Into Paradigms and Challenges for Clinical Applicability.Frontiers in psychiatry · 2020Article
- A Predictive Model Offor Attention Deficit Hyperactivity Disorder Based on Clinical Assessment Tools.Neuropsychiatric disease and treatment · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that has heavy consequences on a child's wellbeing, especially in the academic, psychological and relational planes. The current evaluation of the disorder is supported by clinical assessment and written tests. A definitive diagnosis is usually made based on the DSM-V criteria. There is a lot of ongoing research on ADHD, in order to determine the neurophysiological basis of the disorder and to reach a more objective diagnosis. The advent of Machine Learning (ML) opens up promising prospects for the development of systems able to predict a diagnosis from phenotypic and neuroimaging data. This was the reason why the ADHD-200 contest was launched a few years ago. Based on the publicly available ADHD-200 collection, participants were challenged to predict ADHD with the best possible predictive accuracy. In the present work, we propose instead a ML methodology which primarily places importance on the explanatory power of a model. Such an approach is intended to achieve a fair trade-off between the needs of performance and interpretability expected from medical diagnosis aid systems. We applied our methodology on a data sample extracted from the ADHD-200 collection, through the development of decision trees which are valued for their readability. Our analysis indicates the relevance of the limbic system for the diagnosis of the disorder. Moreover, while providing explanations that make sense, the resulting decision tree performs favorably given the recent results reported in the literature.
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.