Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach
Bibliographic record
Abstract
Abstract Several studies have documented that structural MRI findings are associated with the presence of early-stage Alzheimer Disease (AD). However, the association of each MRI feature with the rate of conversion from mild cognitive impairment (MCI) to AD in a multivariate setting has not been studied fully. The objective of this work is the comprehensive exploration of four different machine learning (ML) strategies to build MRI-based multivariate Cox regression models. These models evaluated the association of MRI features with the time of MCI to clinical AD conversion. We used 442 MCI subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI) study. Each subject was described by 346 MRI features and time to AD conversion. Cox regression models then estimated the rate of conversion. Models were built using four ML methodologies in a cross-validation (CV) setting. All the ML methods returned successful Cox models with different CV performances. The best model exhibited a concordance index of 0.84 (95% CI: 0.82-0.86). The final analysis described the hazard ratios (HR) of the top ten MRI features associated with MCI to AD conversion. Our results suggest ML exploration is a viable strategy for building and analyzing survival models that predict subjects at risk of AD conversion.
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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".