Prediction of Incident Alzheimer's Disease in Older Community‐Dwelling Adults using Wrist Accelerometry and Deep Learning
Bibliographic record
Abstract
Abstract Background Drugs targeting Alzheimer's disease (AD) pathology are likely to be most effective in the presymptomatic stage, where individuals harbor AD pathology but have not manifested symptoms. Neuroimaging approaches can help to identify such individuals, but are costly for population‐wide screening. Cost‐effective screening is needed to identify those who may benefit from neuroimaging, such as those at risk of developing clinical disease. We present a deep learning algorithm that uses accelerometry recordings to predict clinically diagnosed AD in dementia‐free patients. Method Participants were from The Memory and Aging Project (MAP), a longitudinal cohort study of older adults focused on aging and dementia. As part of this study, participants were asked to wear a wrist accelerometer for ten days. We designed a feedforward neural network that synthesizes clinical and accelerometric features to predict clinical AD. Clinical features were selected based on ease of collection, and included age, sex, education, social isolation and purpose in life. The dataset included participants without dementia at the time of recording, who survived for and had a known clinical AD outcome within five years of the recording. Result The training dataset consisted of 875 unique patients. The test dataset consisted of 395 unique patients (mean age 81.5, SD= 6.8). 14.4% of the test set developed clinical AD within five years. On the test set, the model achieved 89% sensitivity (SN), 70% specificity (SP), and an F1 score of 0.87 (Figure 1). This is superior to accelerometric‐only (SN 67%, SP 70%, F1 0.68) and clinical‐only (SN 76%, SP 80%, F1 0.78) models. Model accuracy was similar for patients both with and without mild cognitive impairment at baseline. When the binarized model output was used as a predictor for five‐year AD‐free survival via Cox proportional hazards (Figure 2), it achieved a C‐index of 0.729 [95% CI 0.69 b‐0.77], a hazard ratio of 6.90 [95% CI 4.15‐11.47], and a log‐likelihood ratio of 71.05. Further tuning and validation of the model are underway. Conclusion A deep learning model using wrist accelerometry and easily obtained clinical features shows promise in predicting 5‐year AD outcomes in adults without dementia at baseline.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".