Detection, Monitoring and Follow-up of ADHD suffering children using Deep Learning
Bibliographic record
Abstract
Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects a significant portion of the younger population (0-16 years) worldwide. Early detection, continuous monitoring, and effective follow- up of ADHD in children are very vital for providing timely therapies and enhancing the long-term outcomes of affected individuals. This paper discusses a novel approach that uses deep learning techniques to detect, monitor, and follow up with children suffering from Attention Deficit Hyperactive Disorder (ADHD). The system begins with comprehensive data collection, including behavioral assessments, genetic markers, and biomarker levels. Feature extraction methods are utilized to identify the most relevant attributes linked with different types of ADHD— Inattentive, Hyperactive- Impulsive, and Combined. The deep learning model is trained on these features, with a goal of improving diagnostic accuracy through iterative validation and hyperparameter tuning. The deep learning model is evaluated using standard metrics, such as accuracy, precision, recall, and F1-score, to ensure effective performance. This approach aims to enhance diagnostic precision and support personalized treatment strategies, offering a more individualized therapy pathway for children with ADHD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".