Abstract 21042: Cardiovascular Risk Stratification Using Off-the-Shelf Wearables and a Multi-Task Deep Learning Algorithm
Bibliographic record
Abstract
Introduction: We aimed to evaluate whether a novel deep neural network (DNN) can predict cardiovascular risk factors from off-the-shelf wearables with a photoplethysmographic (PPG) heart rate sensor and accelerometer. Longitudinal heart rate variability and activity patterns have previously been associated with incident hypertension, diabetes, and sleep apnea, conditions which are frequently undiagnosed. Methods: Health eHeart, an IRB-approved UCSF study, enrolled 6,115 active users of the Cardiogram app for Apple Watch. Heart rate and step counts were collected for a period of 1 to 53 weeks (mean=8.9). Data from 70% of participants (33,628 person-weeks of data) was used to train a semi-supervised, multi-task DNN with both convolutional and recurrent layers to simultaneously predict prevalent hypertension, sleep apnea, and diabetes. Test performance characteristics were estimated using the remaining 30% of participants. Results: Mean age was 42.3 ± 12.1, 69% male. 2,230 (36.5%) of participants had hypertension, 1,016 (16.6%) had sleep apnea, and 462 (7.6%) had diabetes. In the validation set, the DNN outperformed a baseline logistic regression model incorporating age, sex, and beta blocker use, predicting prevalent hypertension with a c-statistic of 0.819 (95% CI 0.76-0.88; with an optimal operating point yielding 84.8% sensitivity and 63.6% specificity) vs a baseline c-statistic of 0.682 (95% CI 0.60-0.76), and prevalent sleep apnea with a c-statistic of 0.902 (95% CI 0.85-0.95; with an optimal operating point yielding 90.4% sensitivity and 59.8% specificity) vs a baseline c-statistic of 0.459 (95% CI 0.39-0.53). Results were not statistically significant for diabetes. Conclusions: Our DNN demonstrates surprisingly good prediction of hypertension and sleep apnea given that its only inputs are heart rate and step count. Whether such DNNs can provide durable and portable predictions for these conditions in other study samples is worth pursuing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".