The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A model-based algorithm for blood glucose control in Type I diabetic patients
Abstract
A model-based predictive control algorithm is developed to maintain normoglycemia in the Type I diabetic patient using a closed-loop insulin infusion pump. Utilizing compartmental modeling techniques, a fundamental model of the diabetic patient is constructed. The resulting nineteenth-order nonlinear pharmacokinetic-pharmacodynamic representation is used in controller synthesis. Linear identification of an input-output model from noisy patient data is performed by filtering the impulse-response coefficients via projection onto the Laguerre basis. A linear model predictive controller is developed using the identified step response model. Controller performance for unmeasured disturbance rejection (50 g oral glucose tolerance test) is examined. Glucose setpoint tracking performance is improved by designing a second controller which substitutes a more detailed internal model including state-estimation and a Kalman filter for the input-output representation. The state-estimating controller maintains glucose within 15 mg/dl of the setpoint in the presence of measurement noise. Under noise-free conditions, the model-based predictive controller using state estimation outperforms an internal model controller from literature (49.4% reduction in undershoot and 45.7% reduction in settling time). These results demonstrate the potential use of predictive algorithms for blood glucose control in an insulin infusion pump.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Bergman, R. N., Phillips, L. S., & Cobelli, C. (1981) · Journal of Clinical Investigation · doi:10.1172/jci110398 · Cited by 1,640 · cited 2×
“Many approaches to modeling the human glucose-insulin system have been taken [9], [17]–[19], starting from the initial glucose modeling work of Bolie [20].”
BackgroundMethodCoefficients of normal blood glucose regulation
Bolie, V. W. (1961) · Journal of Applied Physiology · doi:10.1152/jappl.1961.16.5.783 · Cited by 349 · cited 1×
“Many approaches to modeling the human glucose-insulin system have been taken [9], [17]–[19], starting from the initial glucose modeling work of Bolie [20].”
Method
Research works printed but not linked: 35
- a˚stro¨m, 1990, Computer-Controlled Systems Theory and Design
- 10.1002/aic.690371012 · doi:10.1002/aic.690371012
- morari, 1999, model predictive control: ideas for the next generation, IEE Colloquium on Model Predictive Control Techniques and Applications Day 1, 2/1
- 10.1080/00986449708936583 · doi:10.1080/00986449708936583
- parker, 1996, model predictive control for infusion pump insulin delivery, Proc 18th Annu Conf IEEE— Engineering in Medicine and Biology Society, 10.1109/IEMBS.1996.646272 · DOI
- garci´a, 1984, quadratic dynamic matrix control of nonlinear processes. an application to a batch reaction process, AIChE Annu Meet
- 10.1021/ie00004a018 · DOI · Paper page
- 10.1021/ie00099a013 · doi:10.1021/ie00099a013
- 10.1109/10.68209 · DOI · Paper page
- 10.1109/TBME.1985.325500 · DOI
- 10.1109/TBME.1981.324661 · DOI
- kwok, 1992, evaluation of a long-range adaptive predictive controller for computerized drug delivery systems, Proc IFAC Adaptive Signals in Control and Signal Processing Conference, 317
- 10.1021/bp00033a013 · doi:10.1021/bp00033a013
- linkens, 1994, generalized predictive control (gpc) in clinical anaesthesia, Advances in Model-Based Predictive Control, 429
- 10.1109/10.391165 · doi:10.1109/10.391165
- 10.1016/0010-4809(84)90021-1 · DOI
- puckett, 1992, Dynamic modeling of diabetes mellitus
- 10.1109/9.222305 · doi:10.1109/9.222305
- 10.2337/diacare.15.1.53 · DOI
- schetzen, 1980, The Volterra and Wiener Theories of Nonlinear Systems
- cohen, 1993, new disposable electronic micropump for parenteral drug delivery, Pulsatile Drug Delivery Current Applications and Future Trends, 151
- preidel, 1991, an electrocatalytic glucose sensor for in-vivo application, Biomed Instrum Technol, 25, 215
- 10.1007/BF02368639 · DOI · Paper page
- buchwald, 1989, insulin delivery by implanted pump, a chronic treatment for diabetes, ASAIO Trans, 35, 5
- 10.1007/BF02221686 · DOI
- nishida, 1995, development of a ferrocene-mediated needle-type glucose sensor covered with newly designed biocompatible membrane, 2-methacryloyloxyethylphosphorylcholine-co-n-butyl methacrylate, Med Prog through Technol, 21, 91
- 10.2165/00003495-199142020-00004 · DOI
- 10.2337/diacare.13.9.955 · DOI
- 10.1007/BF02442625 · DOI
- 10.2337/diabetes.27.10.1027 · DOI
- 10.1007/BF02477259 · DOI
- 10.1016/0141-5425(92)90058-S · DOI · Paper page
- sorensen, 1985, A physiologic model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes
- 10.1007/978-1-4613-3970-0 · DOI · Paper page
- 10.1080/00207176508905543 · doi:10.1080/00207176508905543
Cited by
Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes
Hovorka, R., Canonico, V., Chassin, L. J., Haueter, U., Massi-Benedetti, M., Federici, M. O., Pieber, T. R., Schaller, H. C., Schaupp, L., Vering, T., et al. (2004) · Physiological Measurement · doi:10.1088/0967-3334/25/4/010 · Cited by 1,274
Artificial Pancreas: Past, Present, Future
Cobelli, C., Renard, E., & Kovatchev, B. (2011) · Diabetes · doi:10.2337/db11-0654 · Cited by 600
Closed-loop insulin delivery from bench to clinical practice
Hovorka, R. (2011) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2011.32 · Cited by 342
Model Predictive Control of Type 1 Diabetes: An in Silico Trial
Magni, L., Raimondo, D. M., Bossi, L., Dalla Man, C., De Nicolao, G., Kovatchev, B., & Cobelli, C. (2007) · Journal of Diabetes Science and Technology · doi:10.1177/193229680700100603 · Cited by 317
Kovatchev, B., Cobelli, C., Renard, E., Anderson, S., Breton, M., Patek, S., Clarke, W., Bruttomesso, D., Maran, A., Costa, S., et al. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400611 · Cited by 200
Metadata
- Authors
- Parker, R. S., Doyle, F. J., & Peppas, N. A.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 1999
- Volume
- 46
- Issue
- 2
- Pages
- 148-157
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2142962347
- Cited by
- 500
- References
- 2
- FWCI
- 3.62
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A model-based algorithm for blood glucose control in Type I diabetic patients. https://app.cochat.ai/papers/10.1109/10.740877
- The paper
- Parker, R. S., Doyle, F. J., & Peppas, N. A. (1999). A model-based algorithm for blood glucose control in Type I diabetic patients. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/10.740877
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.