Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study
Bibliographic record
Abstract
Dementia due to Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by various cognitive and behavioral decline factors. In this work, we propose an extension of the traditional k-means clustering for multivariate time series data to cluster joint trajectories of different features describing progression over time. The algorithm we propose here enables the joint analysis of various longitudinal features to explore co-occurring trajectory factors among markers indicative of cognitive decline in individuals participating in an AD progression study. By examining how multiple variables co-vary and evolve together, we identify distinct subgroups within the cohort based on their longitudinal trajectories. Our clustering method enhances the understanding of individual development across multiple dimensions and provides deeper medical insights into the trajectories of cognitive decline. In addition, the proposed algorithm is also able to make a selection of the most significant features in separating clusters by considering trajectories over time. This process, together with a preliminary pre-processing on the OASIS-3 dataset, reveals an important role of some neuropsychological factors. In particular, the proposed method has identified a significant profile compatible with a syndrome known as Mild Behavioral Impairment (MBI), displaying behavioral manifestations of individuals that may precede the cognitive symptoms typically observed in AD patients. The findings underscore the importance of considering multiple longitudinal features in clinical modeling, ultimately supporting more effective and individualized patient management strategies.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".