LMP-TX: An AI-driven Integrated Longitudinal Multi-modal Platform for Early Prognosis of Late Onset Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Alzheimer’s Disease (AD) is the 7th leading cause of death worldwide. 95% of AD cases are late-onset Alzheimer’s disease (LOAD), which often takes decades to evolve and become symptomatic. Early prognosis of LOAD is critical for timely intervention before irreversible brain damage. This study proposes an Artificial Intelligence (AI)-driven longitudinal multi-modal platform with time-series transformer (LMP-TX) for the early prognosis of LOAD. It has two versions: LMP-TX utilizes full multi-modal data to provide more accurate prediction, while a lightweight version, LMP-TX-CL, only uses simple multi-modal and cognitive-linguistic (CL) data. Results on prognosis accuracy based on the AUC scores for subjects progressing from normal control (NC) to early mild cognitive impairment ( e MCI) and e MCI to late MCI ( l MCI) is respectively 89% maximum (predicted by LMP-TX) and 81% maximum (predicted by LMP-TX-CL). Moreover, results on the top biomarkers predicting different states of LOAD onsets have revealed key multi-modal (including CL-based) biomarkers indicative of early-stage LOAD progressions. Future work will develop a more fine-grained LMP-TX based on disease progression scores and identify the key multi-modal and CL-based biomarkers predictive of fast AD progression rates at early stages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".