Initial Results in Alzheimer's Disease Progression Modeling Using Imputed Health State Profiles
Bibliographic record
Abstract
This paper describes an initial step in developing a set of quasi-patient profiles, each representing a complete longitudinal medical history of Alzheimer's disease (AD) – from normal health to the clinical emergence of the disease and beyond. Quasi-patient is the term given to a unified medical record created through the optimal imputation of individual records, and the guided merger and completion of multiple patient records from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In the present paper, imputation strategies and boosted ensemble decision trees are used to characterize the health states of patients in the ADNI database which consistently yield year-by-year health state predictions of 80% or greater accuracy. In addition, relative to ordinarily ignoring missing medical records in a patient's history, imputation and state estimation guided by globally-optimal decision criteria resulted in an accuracy increase from 76.1% to 81.9%.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".