Correlation-Aware Sparse and Low-Rank Constrained Multi-Task Learning for Longitudinal Analysis of Alzheimer's Disease
Bibliographic record
Abstract
Alzheimer's disease (AD), as a severe neurodegenerative disease, is now attracting more and more researchers' attention in the healthcare. With the development of magnetic resonance imaging (MRI), the neuroimaging-based longitudinal analysis is gradually becoming an important research direction to understand and trace the process of the AD. In addition, regression analysis has been commonly adopted in the AD pattern analysis and progression prediction. However, most existing methods assume that all input features are equally related to the output variables, which ignore the difference in terms of the correlation. In this paper, we proposed a novel multi-task learning formulation, which considers a correlation-aware sparse and low-rank constrained regularization, for accurately predicting the cognitive scores of the patients at different time points and identifying the most predictive biomarkers. In addition, an efficient iterative algorithm is developed to optimize the proposed non-smooth convex objective formulation. We also have performed experiments using data from the AD neuroimaging initiative dataset to evaluate the proposed optimization formulation. Especially, we will predict cognitive scores of multiple time points through the baseline MRI features. The results not only indicate the rationality and correctness of the proposed method for predicting disease progression but also identify some stable and important MRI features that are consistent with the previous research.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".