Correction: Machine learning on a smartphone-based CPT for ADHD prediction
Núria Casals, Simon Larsson, Mikkel Hansen
Abstract
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAttention Deficit Hyperactivity Disorder · EEG and Brain-Computer Interfaces · Health, Environment, Cognitive Aging
In the Abstract, paragraph 3, a specificity value was missing due to a typographical error. This paragraph read as, “A total of 952 neurotypical individuals and 292 unmedicated ADHD patients were part of the study. The best performing model combines all feature groups by a sensitivity of 0.808, specificity of blue and area under the precision-recall curve (PR-AUC) of 0.799, with a considerable performance increase due to the phone sensor features addition. Results did not differ significantly by age group (6–11 and 12–60 years old) and sex.” The corrected paragraph should read as, “A total of 952 neurotypical individuals and 292 unmedicated ADHD patients were part of the study. The best performing model combines all feature groups by a sensitivity of 0.808, specificity of 0.795 and area under the precision-recall curve (PR-AUC) of 0.799, with a considerable performance increase due to the phone sensor features addition. Results did not differ significantly by age group (6–11 and 12–60 years old) and sex.”
The original version of this article has been updated.