Adaptive Parametric Tuning of Glucose-Insulin Kinetics Models Using Multilayer Perceptrons
Bibliographic record
Abstract
Modeling of human glucose-insulin kinetics is a complex non-linear multivariate problem. Models are required to represent the dynamics of the human glucose-insulin system and account for multiple edge cases with clinically accepted levels of accuracy. State of the art systems typically incorporate data from clinical trials of large cohorts in order to produce model parameters. Their ability to produce highly accurate results on a per-cohort basis have encouraged researchers to explore the possibility of extending their utility to individual patients. Among these research efforts, machine learning techniques such as support vector machines have been employed with varying levels of success. This paper proposes a novel adaptive method of generating patient-specific model parameters using multilayer perceptrons. A clinically accepted physiological model of glucose-insulin kinetics is used to generate features that are passed to a multilayer perceptron. Comparison of the predicted versus actual parameters of the model indicate that the MLP method is highly accurate and can be utilized to calibrate physiological models to specific patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".