Model-based detection of organ dysfunction and faults in insulin infusion devices for type 2 diabetic patients
Bibliographic record
Abstract
Controlling blood glucose level for patient with type 2 diabetes mellitus (T2DM) has been influenced by many variables with significant levels of variability, such as insulin sensitivity, carbohydrates intake, exercise, and more. These variabilities make controlling blood glucose level a complex problem. In patients with advanced T2DM, when the body fails to regulate blood glucose level, an external loop including an insulin pump and a glucose measurement device can be used in maintaining glucose regulation. However, the control of blood glucose level may fail even in patient with insulin pump therapy. Therefore, building a reliable model-based fault detection system to detect failures in controlling blood glucose level dealing with all variability, uncertainty, and lack of accuracy is critical. In this article, we propose utilizing a validated robust model-based fault detection technique based on a Sequential Monte Carlo (SMC) filtering method for detecting faults in the insulin infusion system and detecting patient's organ dysfunction. For detecting faults, a previously validated metabolic model of glucose regulation in T2DM is used. Our results show that the proposed technique is capable of detecting disconnection in insulin infusion systems and detecting peripheral and hepatic insulin resistance.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".