Predicting Future Glucose Fluctuations Using Machine Learning and Wearable Sensor Data
Bibliographic record
Abstract
Predicting blood glucose values (BG) using machine learning (ML) algorithms and data fusion techniques. There has been a recent explosion of interest in BG prediction due to its application in the development of insulin regulating algorithms for the Artificial Pancreas Project. During this study, we measured the predictive accuracy of a software system designed to predict glucose behaviour using step-count and heart-rate data in addition to BG-insulin dynamics. The software was tested in a blinded pilot study at BC Children's Hospital for 9 type 1 diabetic children. Using continuous glucose monitors (CGM) and fitness wearables (Fitbit), the software aggregated 60-days of continuous data from each participant. The data from the first 30-days of the study was used to train the algorithem. The trained algorithm was then used to make predictions every 5mins for the next 30days. On average, the software was able to predict user's future glucose values with 93% accuracy rate for 60-mins ahead of time. Although encouraging, the algorithm required further testing. We have since released the app under the commercial name "DiaBits" for more testing and further data collection. Disclosure A. Hayeri: Other Relationship; Self; Dexcom, Inc., Fitbit, Inc.. Research Support; Self; BC Children's Hospital - Vancouver Canada. Other Relationship; Self; University Of British Columbia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".