Moving horizon estimation for continuous glucose monitoring
Bibliographic record
Abstract
Continuous glucose monitoring (CGM) systems have great advantages for the treatment of diabetes.Common commercial sensors are placed in the subcutaneous tissue and continuously measure (1-5 min) a signal correlated to the glucose concentration in the interstitial fluid (ISF).As the glucose concentration in the ISF and the blood compartment differ, especially after meal or insulin injection, blood glucose (BG) estimation methods improve the consistency of CGM sensor glucose values and standard blood glucose measurements.However, reference measurements (selfmonitoring blood glucose measurements) are typically required for the calibration of CGM sensors.Thus, the sensor calibration also depends on the quality of BG estimation.In this paper we present a BG estimation method based on moving horizon estimation combined with an adaptive noise variance estimation.Compared with the common estimation methods, Kalman filtering and signal smoothing, the presented method achieves an improve of BG estimation.In addition, the method leads to an improved estimation of past BG values, leading to better calibration results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".