Development of Simple Risk Scores for Prediction of Brain β-Amyloid and Tau Status in Older Adults With Mild Cognitive Impairment: A Machine Learning Approach
Bibliographic record
Abstract
OBJECTIVES: The aim of this work is to use a machine learning framework to develop simple risk scores for predicting β-amyloid (Aβ) and tau positivity among individuals with mild cognitive impairment (MCI). METHODS: Data for 657 individuals with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set were used. A modified version of AutoScore, a machine learning-based software tool, was used to develop risk scores based on hierarchical combinations of predictor categories, including demographics, neuropsychological assessments, APOE4 status, and imaging biomarkers. RESULTS: The highest area under the receiver operating characteristic curve (AUC) for predicting Aβ positivity was 0.79, which was achieved by 2 separate models with predictors of age, Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-cog), APOE4 status, and either Trail Making Test Part B (TMT-B) or white matter hyperintensity. The best-performing model for tau positivity had an AUC of 0.91 using age, ADAS-13, and TMT-B scores, APOE4 information, abnormal hippocampal volume, and amyloid status as predictors. DISCUSSION: Simple integer-based risk scores using available data could be used for predicting Aβ and tau positivity in individuals with MCI. Models have the potential to improve clinical trials through improved screening of individuals.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.001 | 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".