Detection, Monitoring and Follow-up of ADHD suffering children using Deep Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it