Detecting label noise in longitudinal Alzheimer’s data with explainable artificial intelligence
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
Reliable classification of cognitive states in longitudinal Alzheimer's Disease (AD) studies is critical for early diagnosis and intervention. However, inconsistencies in diagnostic labeling, arising from subjective assessments, evolving clinical criteria, and measurement variability, introduce noise that can impact machine learning (ML) model performance. This study explores the potential of explainable artificial intelligence to detect and characterize noisy labels in longitudinal datasets. A predictive model is trained using a Leave-One-Subject-Out validation strategy, ensuring robustness across subjects while enabling individual-level interpretability. By leveraging SHapley Additive exPlanations values, we analyze the temporal variations in feature importance across multiple patient visits, aiming to identify transitions that may reflect either genuine cognitive changes or inconsistencies in labeling. Using statistical thresholds derived from cognitively stable individuals, we propose an approach to flag potential misclassifications while preserving clinical labels. Rather than modifying diagnoses, this framework provides a structured way to highlight cases where diagnostic reassessment may be warranted. By integrating explainability into the assessment of cognitive state transitions, this approach enhances the reliability of longitudinal analyses and supports a more robust use of ML in AD research.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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