Cited by 511Diabetes Management and ResearchPancreatic function and diabetesDiabetes Management and Education

The DOI matches its Crossref record; 305 of the 305 references it cites were found in a registry or an index.

Diabetes: Models, Signals, and Control

Open access

Cobelli, C., Dalla Man, C., Sparacino, G., Magni, L., De Nicolao, G., Kovatchev, B.P.·2009·IEEE Reviews in Biomedical Engineering

DOI 10.1109/rbme.2009.2036073PMC PMC2951686

Open this paper in CoChat — free account

Abstract

The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.

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

Crossref · 10.1109/rbme.2009.2036073

Cites

305 of 305 references verified0 citation errors · 0 not checkable

Statistics

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.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Glucose clamp technique: a method for quantifying insulin secretion and resistance.
DeFronzo, R. A., Tobin, J. D., & Andres, R. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.237.3.e214 · Cited by 7,425 · cited 1×
“In numerous studies reviewed in [27], insulin sensitivity estimated with the minimal model has been shown to strongly correlate to that measured with the euglycemic-hyperinsulinemic clamp [36], calculated as the steady-state glucose infusion rate divided by basal glucose times the above basal increase in insulin conce…”
THE [ 14 C]DEOXYGLUCOSE METHOD FOR THE MEASUREMENT OF LOCAL CEREBRAL GLUCOSE UTILIZATION: THEORY, PROCEDURE, AND NORMAL VALUES IN THE CONSCIOUS AND ANESTHETIZED ALBINO RAT 1
Sokoloff, L., Reivich, M., Kennedy, C., Rosiers, M. H. D., Patlak, C. S., Pettigrew, K. D., Sakurada, O., & Shinohara, M. (1977) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.1977.tb10649.x · Cited by 6,000
Global and societal implications of the diabetes epidemic
Zimmet, P., Alberti, K. G. M. M., & Shaw, J. (2001) · Nature · doi:10.1038/414782a · Cited by 5,883 · cited 2×
“Rapid urbanization and societal affluence of global migrating populations has been suggested as major risk factors for the observed exploding prevalence of prediabetes and type 2 diabetes with consequent rising trends in cardiovascular risks [8].”
Approximate entropy as a measure of system complexity.
Pincus, S. M. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.6.2297 · Cited by 5,811 · cited 1×
“3) Hormone Pulsatility and Approximate Entropy: Approximate Entropy (ApEn) is a method developed in the early 1990s [171] to quantify the “regularity” of a time series.”
Quantitative estimation of insulin sensitivity.
Bergman, R. N., Ider, Y. Z., Bowden, C. R., & Cobelli, C. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.236.6.e667 · Cited by 1,874 · cited 33×
“In fact, during IVGTT first phase component only contributed during the first 4–6 minutes and the proportional component for the rest of test.”
InfluentialSelf-citation
Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose
Bergman, R. N., Phillips, L. S., & Cobelli, C. (1981) · Journal of Clinical Investigation · doi:10.1172/jci110398 · Cited by 1,640 · cited 3×
“One possibility is to resort to a normalization of beta-cell function based on the disposition index paradigm, first introduced in 1981 [35], and recently revisited in [28], where beta-cell function is multiplied by insulin sensitivity.”
Self-citation
Continuous glucose monitoring and intensive treatment of type 1 diabetes
(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0805017 · Cited by 1,559
Glucose tolerance and mortality: comparison of WHO and American Diabetic Association diagnostic criteria
(1999) · The Lancet · doi:10.1016/s0140-6736(98)12131-1 · Cited by 1,313
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 · cited 2×
“The model [136] has been built for being the model in-”
Glucose tolerance and mortality: comparison of WHO and American Diabetes Association diagnostic criteria
Borch‐Johnsen, K., Neil, A., Balkau, B., Larsen, S. H., Nissinen, A. E. T., Pekkanen, J., Keinanen-Kiukaanniemi, S. M., Hiltunen, L. A., Kivelä, S., Tuomilehto, J. O., et al. (1999) · The Lancet · Cited by 1,272

Flagged first, then by citations

Show all 304 references

Research works printed but not linked: 15

10.1210/jc.80.1.28 · DOI
10.1210/jc.81.10.3746 · DOI
patek, 2007, Adaptive meal detection algorithm enabling closed-loop control in type 1 diabetes, Proc Diabetes Technology Meeting
10.1210/jc.82.12.4088 · DOI
10.1210/jc.84.6.1938 · DOI
bassingthwaighte, 1984, Handbook of Physiology The Cardiovascular System Microcirculation
utriainen, 1998, Lumped constant for [14C]deoxy-D-glucose in human skeletal muscle, Diabetologia, 41, 187a
palerm, 2005, Hypoglycemia prediction and detection using optimal estimation, Diabetes Technol Therapeutics, 7, 3, 10.1089/dia.2005.7.3 · DOI
10.1177/193229680700100505 · DOI
dalla man, 2009, A pre & type 2 diabetes simulator for in silico trials, Proc Ninth Diabetes Technology Meeting
pickup, 1991, Textbook of Diabetes, 2
denti, 2009, A nonlinear mixed-effects approach to the estimation of the glucose disposition index, Proc PAGE 2009 Meeting
toffolo, 2001, Quantitative indexes of beta-cell function during graded up&down glucose infusion from C-peptide minimal models, Amer J Physiol Endocrinol Metab, 280, 2e, 10.1152/ajpendo.2001.280.1.E2 · DOI · Paper page
patek, 2009, Control of hypoglycemia via estimation of active insulin, glucose forecasts, and risk-based insulin reduction, Proc 2nd Advanced Technol Treatment for Diabetes
2009, Nonlinear Model Predictive Control Towards New Challenging Applications, 384

Sorted by citations

Items 401–500 of 509

Understanding Immune Dynamics in Liver Transplant Through Mathematical Modeling
Bruner, J., Adams, K., Grey, S., Aghaee, M., Duarte, S. M., Zarrinpar, A., & Moore, H. A. (2025) · Bulletin of Mathematical Biology · doi:10.1007/s11538-025-01480-8 · Cited by 1
Online Imputation of Corrupted Glucose Sensor Data Using Deep Neural Networks and Physiological Inputs
Sánchez, O. D., Mendez-Palos, E., Pascoe, D. A., Hernandez, H. M., Alvarez, J. G., & Alanís, A. Y. (2025) · Algorithms · doi:10.3390/a18110688 · Cited by 1
Toward a Personalized Basal Tuner for Detecting Basal Rate Inaccuracies in Type 1 Diabetes Mellitus Without Meal Data: Algorithm Development and Retrospective Validation Study
García, D. G., Thabit, H., Nutter, P. W., & Harper, S. (2025) · JMIR Diabetes · doi:10.2196/72769 · Cited by 1
Platform design of hybrid microphysiological system integrating in vitro and in silico models for reproducing insulin-regulated glucose metabolism
Konishi, S., Kotera, S., Minamikawa, T., Yamashita, F., & Fujita, T. (2026) · Scientific Reports · doi:10.1038/s41598-026-49605-w · Cited by 1
Competitive catabolism drives hyperglycemia and hyperinsulinemia in obesity
Weilandt, D., Lee, W. D., MacArthur, M. R., Liang, L., Holman, C. D., Chu, Q., Cowan, A. J., Baur, J. A., Wingreen, N. S., & Rabinowitz, J. D. (2026) · Cell Metabolism · doi:10.1016/j.cmet.2026.07.001 · Cited by 1
A Model Approach: Mathematical modeling provides an increasingly clear picture of glucose and neural systems
Cobelli, C., & Ursino, M. (2015) · IEEE Pulse · doi:10.1109/mpul.2015.2428681 · Cited by 1
Self-citation
Embedded implementation of modular closed-loop control of diabetes and in silico validation
Gentili, M., Caltabiano, D., Sannino, R., Toffanin, C., Palma, F. D., Magni, L., & Lane, S. (2013) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2013.6720730 · Cited by 1
Self-citation
Online redesign of clinical tests for the identification of type 1 diabetes models in the presence of continuous glucose monitoring systems
Galvanin, F., Barolo, M., & Bezzo, F. (2011) · IFAC Proceedings Volumes · doi:10.3182/20110828-6-it-1002.01120 · Cited by 1
Model of the glucose-insulin system of type-1 diabetics and optimization-based bolus calculation
Utz, T., Braun, M., Graichen, K., & Freckmann, G. (2014) · UKACC International Conference on Control · doi:10.1109/control.2014.6915204 · Cited by 1
Systems biology of mammalian cells: A report from the Freiburg conference
Timmer, J., HENNEY, A. M., Moore, A., & Klingmüller, U. (2010) · BioEssays · doi:10.1002/bies.201000109 · Cited by 1
Diabetic Blood Glucose Control via Optimization over Insulin and Glucose Doses
Stemmann, M., & Johansson, R. (2012) · IFAC Proceedings Volumes · doi:10.3182/20120829-3-hu-2029.00069 · Cited by 1
Predictive-retrospective proportional glycemic control for type 1 diabetes mellitus
Yan, C., Wang, Y., & Sun, X. (2013) · ICME International Conference on Complex Medical Engineering (CME) · doi:10.1109/iccme.2013.6548336 · Cited by 1
Applicability of asymptotic tracking in case of type 1 diabetes
Szalay, P., & Kovács, L. (2011) · IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) · doi:10.1109/saci.2011.5873078 · Cited by 1
Detection of Events using Continuous Glucose Monitors
Pejtersen, M., Karen, S., & Lyngby, K. (2016) · Cited by 1
A Multifactor Model of Blood Glucose Concentration Dynamics
Novikov, I. A., Yurkov, I. V., & Komarovskaia, A. I. (2016) · Biomedical Engineering · doi:10.1007/s10527-016-9561-y · Cited by 1
Can We Use Measurements to Classify Patients Suffering from Type 1 Diabetes into Subcategories and Does It Make Sense?
Reiterer, F., Kirchsteiger, H., Freckmann, G., & del Re, L. (2015) · Lecture notes in bioengineering · doi:10.1007/978-3-319-25913-0_4 · Cited by 1
The Development Of A Virtual Sensor In Glucose Monitoring For Non-Insulin Dependent People
Rollins, D. K., Beverlin, L., Mei, Y., Kotz, K., André, D., Vyas, N., Welk, G. J., & Franke, W. D. (2014) · Journal of Bioinformatics and Diabetes · doi:10.14302/issn.2374-9431.jbd-13-283 · Cited by 1
C code generation applied to nonlinear model predictive control for an artificial pancreas
Boiroux, D., & Jørgensen, J. B. (2017) · International Conference on Process Control · doi:10.1109/pc.2017.7976235 · Cited by 1
ARX MPC for people with type 1 diabetes
Boiroux, D., Finan, D. A., Jørgensen, J. B., Poulsen, N. K., & Madsen, H. (2010) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · Cited by 1
Hierarchical Simulation. Algorithm for Prediction of Glycemic profile for Diabetes
Kiforenko, S. I., Vasyliev, I., Орленко, В. Л., Ivaskiva, К. Y., & Обелець, Т. А. (2020) · Cybernetics and computer engineering · doi:10.15407/kvt201.03.065 · Cited by 1
Algorithm for Predicting the Glicemic Profil in Diabetes Under Regular Measurements
Kiforenko, S. I., Vasyliev, I., Lavrenyuk, M. V., & Гонтарь, Т. С. (2021) · Innovative Biosystems and Bioengineering · doi:10.20535/ibb.2021.5.1.218259 · Cited by 1
Drug–Disease Model-Based Development of Therapeutic Agents for Treatment of Diabetes
Garhyan, P., Topp, B. G., Chien, J. Y., Sinha, V. P., Danhof, M., & Schmidt, S. (2014) · AAPS advances in the pharmaceutical sciences series · doi:10.1007/978-1-4939-1304-6_5 · Cited by 1
Novel individual metabolic profile characterizes the protein kinase B-alpha (pkbα−/−) in vivomodel
Eberle, C., Nießen, M., Hemmings, B. A., Tschopp, O., & Ament, C. (2014) · Archives of Physiology and Biochemistry · doi:10.3109/13813455.2014.911330 · Cited by 1
Computer Simulation Model to Train Medical Personnel on Glucose Clamp Procedures
Maghoul, P., Boulet, B., Tardif, A., & Haidar, A. (2017) · Canadian Journal of Diabetes · doi:10.1016/j.jcjd.2017.08.004 · Cited by 1
Physiological Models and Exogenous Input Modeling
Georga, E. I., Fotiadis, D. I., & Tigas, S. K. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-804831-3.00004-2 · Cited by 1
Kalman filter based nonlinear estimators of glucose and insulin concentrations
Xu, S., Dong, Y., & Zhang, K. (2017) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/compcomm.2017.8322807 · Cited by 1
Role of Automated Insulin Delivery (Artificial Pancreas) in Islet Transplantation: An In Silico Assessment
Lv, D., Garcia‐Tirado, J., & Fabris, C. (2018) · OBM Transplantation · doi:10.21926/obm.transplant.1803019 · Cited by 2
Interval Simulator of the Glucose-Insulin System
Maira, G., Vargas, F. M. L., Rosales, N., Molano-Jimenez, A., & Garelli, F. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-31019-6_58 · Cited by 1
[Glucose metabolism modeling of diabetes patients with different intensities of aerobic exercise: an in silico study].
Jin, J., Yu, L., & Jin, Z. (2019) · PubMed Central · doi:10.7507/1001-5515.201805066 · Cited by 1
Modeling the SMBG measurement error
Vettoretti, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816714-4.00005-3 · Cited by 1
Multi-Step Approach for Sensitivity Analysis for a Unified Model of Glucose-Insulin Metabolism
Tolks, C., Ament, C., & Eberle, C. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2020.12.680 · Cited by 1
Modeling the Effect of Subcutaneous Lixisenatide on Glucoregulatory Endocrine Secretions and Gastric Emptying in Type 2 Diabetes to Simulate the Effect of iGlarLixi Administration Timing on Blood Sugar Profiles
Gautier, T., Silwal, R., Saremi, A., Boss, A., & Breton, M. D. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211015671 · Cited by 1
Introduction
Ho, Y. (2018) · Springer theses · doi:10.1007/978-981-13-2402-4_1 · Cited by 1
Propuesta de un algoritmo de control en lazo cerrado para la diabetes tipo 1
Rodríguez-Herrero, A. (2010) · doi:10.20868/upm.thesis.3681 · Cited by 1
Response to Comment on Kovatchev and Cobelli. Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes. Diabetes Care 2016;39:502–510
Kovatchev, B. P., & Cobelli, C. (2016) · Diabetes Care · doi:10.2337/dci16-0019 · Cited by 1
Self-citation
Medical Cyber-Physical Systems: Challenges and Future Directions
Lee, I., Sokolsky, O. V., & Weimer, J. E. (2021) · Encyclopedia of Systems and Control · doi:10.1007/978-3-030-44184-5_100115 · Cited by 1
Artificial Pancreas: The First 20 Years*
Doyle, F. J., & Kovatchev, B. P. (2025) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2025.0134 · Cited by 2
Self-citation
The Use of Advanced Statistical Concepts and Analysis to Improve Nonlinear Dynamic Glucose Modeling
Beverlin, L. (2011) · doi:10.31274/etd-180810-1238
Identification of complex models of type 2 diabetes from IVGTT data by model-based design of experiments
Galvanin, F., Barolo, M., Bonadonna, R. C., & Bezzo, F. (2013) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/b978-0-444-63234-0.50023-3
A Learning Strategy for the Autonomous Control of Type 1 Diabetes
Fravolini, M. L., Cascianelli, S., & Fabietti, P. G. (2015) · Applied Artificial Intelligence · doi:10.1080/08839514.2015.1038431
Glucose Modelling
Man, C. D., & Cobelli, C. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-411557-6.00016-1
Self-citation
Automatic physical activity pattern recognition and meal intake modelling in type 1 diabetes for their inclusion in artificial pancreas systems
García-García, F. (2015) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.37805
AP@home: An European Project to Bring the Artificial Pancreas Home
Heinemann, L. (2012) · IFAC Proceedings Volumes · doi:10.3182/20120829-3-hu-2029.00116
Insulin Dosage Optimization Based on Model Predictive Control with the Model Parameters Estimation
Zhang, S. S., Zhong, M., & Liu, Y. X. (2014) · Advanced materials research · doi:10.4028/www.scientific.net/amr.971-973.1222
Insulin pump testing platform for robust control framework
Eigner, G., Sas, P. I., & Kovács, L. (2013) · IEEE International Conference on Computational Cybernetics (ICCC) · doi:10.1109/icccyb.2013.6617588
Model-Based Disease Treatment: A Control Engineering Approach
Kovács, L. (2014) · Topics in intelligent engineering and informatics · doi:10.1007/978-3-319-05945-7_3
Inference of stochastic nonlinear equations for characterisation and prediction of prandial blood glucose levels
Zhang, Y., Khovanova, N. A., & Holt, T. (2014) · Warwick Research Archive Portal (University of Warwick) · doi:10.82444/warw.33650326
Clinical risk measure for variability analysis and real-time prevention of hyper/hypo-glycaemic episodes from continuous glucose monitoring time-series
Guerra, S. (2012) · Padua Research Archive (University of Padova)
Cross-Generational Diabetic Programming Using an Established LDLR -/- Mouse Model: Maternal Pre-Gestational Immunization Improves Glucose Homeostasis in their Offspring
Eberle, C., & Ament, C. (2016)
Intraocular Pressure Model Predictive Control: A Simulation Study
Hamid, O. (2016)
Modelling Type 1 Diabetes Mellitus Blood Glucose Dynamics as a Monotone System
Sootla, A., & Cescon, M. (2016) · ORBi (University of Liège)
UNIVERSITA' DEGLI STUDI DI PADOVA FACOLTA' DI INGEGNERIA
Bagno, A. (2012)
Relación dinámica entre la Glucosa Intersticial y la Glucosa Plasmática Usando Modelos Matemáticos del tipo Entrada - Salida
Pérez, A. M. L., Garcia‐Tirado, J., & Lema-Pérez, L. (2016) · Scientia et technica · doi:10.22517/23447214.13151
Personalization of a compartmental physiological model for an artificial pancreas through integration of patient's state estimation
Jallon, P., Lachal, S., Franco, C., Charpentier, G., Huneker, E., & Doron, M. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037108
Development of a Visual Interface of Glucose Metabolism Based on Compartment Mathematical Models
Olay-Blanco, A., & Quiroz-Compeán, G. (2017) · Revista Mexicana de Ingeniería Biomédica · doi:10.17488/rmib.38.3.9
Smart Healthcare Management Framework
Reddy, D. V., & Harekal, D. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-5272-9_28
Development of a model predictive controller for an artificial pancreas
Hachimi, M. E., Ballouk, A., Khelafa, I., & Baghdad, A. (2017) · International Conference on Electrical and Information Technologies · doi:10.1109/eitech.2017.8255244
Risks reduction control techniques analysis in type-1 diabetes
Arif, H., Shuja, S., Imtiaz, S., Khaqan, A., Hasan, Q. U., Malik, S. A., Ahmed, J., & Riaz, R. A. (2018) · Biomedical Research - India · doi:10.4066/biomedicalresearch.29-17-3740
Design of an Implantable Artificial Pancreas
Ho, Y. (2018) · Springer theses · doi:10.1007/978-981-13-2402-4_4
Plasma Glucose Prediction and its Application to Low Glucose Suspension Systems
Rosales, N., Battista, H. D., & Garelli, F. (2018) · Argentine Conference on Automatic Control (AADECA) · doi:10.23919/aadeca.2018.8577384
Physiological Cybernetics: Methods and Applications
Iacoviello, D. (2019) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-030-23073-9_9
Information Analysis of Biochemical Parameters for Glucose Tolerance Tests
Sokol, Y., Chmykhova, O., Boyko, V. V., Zamyatin, P., & Zamiatin, D. (2019) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-030-31866-6_99
Diabetes Monitoring System
Das, S. (2019) · CRC Press eBooks · doi:10.1201/9780429113390-13
[Investigation of the glucose dynamics with an approach of refined composite multi-scale entropy analysis].
Lai, Y., Xin, Y., Gu, W., Zhang, N., Li, P., & Zhang, Z. (2017) · PubMed Central · doi:10.7507/1001-5515.201606015
Following glucose profiles of T1DM UVA/Padova in silico patients, by using estimated carbohydrate intake in a MPC controller
Padilla, D., & Villamizar, R. (2020) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit49905.2020.9263812
Compensación del efecto de variaciones fisiológicas en la glucemia de pacientes diabéticos tipo 1 utilizando control predictivo con entradas impulsivas
Tamayo, V., & Fernánda, M. (2021)
A Model-free Approach to Automatic Dose Guidance in Long Acting Insulin Treatment of Type 2 Diabetes
Krishnamoorthy, D., Boiroux, D., Aradóttir, T. B., Engell, S. E., & Jørgensen, J. B. (2021) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc50511.2021.9483269
A Novel Method for Generation of In Silico Subjects with Type 2 Diabetes
VISENTIN, R., & Lazzari, M. D. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629678
A systematic, well-formulated control system design and modeling paradigm based on perceived system traits
Candelino, N. W. (2019) · doi:10.17760/d20324042
Literature Review
Ho, Y. (2018) · Springer theses · doi:10.1007/978-981-13-2402-4_3
Medical Cyber-Physical Systems: Challenges and Future Directions
Lee, I., Sokolsky, O. V., & Weimer, J. E. (2020) · Encyclopedia of Systems and Control · doi:10.1007/978-1-4471-5102-9_100115-1
Applicability of Asymptotic Tracking in Case of Type 1 Diabetes
Szalay, P., & Kovács, L. (2012) · Topics in intelligent engineering and informatics · doi:10.1007/978-3-642-28305-5_20
Introduction
Świerniak, A., Kimmel, M., Śmieja, J., Puszyński, K., & Psiuk-Maksymowicz, K. (2016) · Springer eBooks · doi:10.1007/978-3-319-28095-0_1
Editorial: Recent Advances in Computer Simulation for Diabetes Treatment and Care
Colmegna, P. H., Toffanin, C., Cescon, M., & VISENTIN, R. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.914657
Introduction
Nath, A., Dey, R., & Bălaş, V. E. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-390776-7.00008-4
Control-oriented modeling of type 1 diabetic patients
Nath, A., Dey, R., & Bălaş, V. E. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-390776-7.00010-2
Investigation on modelling and identification of glucose management system for normal individual and diabetic patient
Sujatha, V. (2022) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.108077
Glucose Controlin Type 2 Diabetes: In Silico Study of Sub-Optimal Adherence to Insulin Therapy
VISENTIN, R., Cobelli, C., Sieber, J., & Man, C. D. (2022) · International Conference on Systems and Control (ICSC) · doi:10.1109/icsc57768.2022.9993828
Self-citation
Research on Blood Glucose Regulation of Type I Diabetes Based on Adaptive RBF Network
Liu, Y., & Sun, K. (2022) · China Automation Congress (CAC) · doi:10.1109/cac57257.2022.10055527
Machine Learning Perspectives in Glucose Level Monitoring and Control (Preprint)Preprint
Mroszczak, M., Mroskowiak, A., Jarosz-Chobot, P. A., & Seget, S. (2023) · doi:10.2196/preprints.52797
Modeling, Estimation, and Feedback Techniques in Type 2 Diabetes
Ahdab, M. A. (2023) · VBN Forskningsportal (Aalborg Universitet) · doi:10.54337/aau614554303
Model predictive control of blood glucose in critically ill patients using Gaussian processes
Benner, C.-F., Weber, N., Leonhardt, S. K., & Walter, M. (2024) · at - Automatisierungstechnik · doi:10.1515/auto-2024-0211
Data analytic models of patients dependent on insulin treatment
Adetuyi, T. G., Adetuyi, B. O., Okunlola, F. O., Oni, P. G., Olajide, P. A., Omolehin, P. T., Adetunji, C. O., Adetunji, M. C., & Oyewole, O. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95686-4.00017-4
Mixted Optimization With Multi-Peaks Diet for Controlling the Glycemic Profil of the Diabetic Patients
Moutaouakil, K. E., Ez-zaiym, M., Ouissari, A. E., Rajafilla, C., & Baïzri, H. (2025) · Revista de Gestão Social e Ambiental · doi:10.24857/rgsa.v19n1-133
Drug-Dose-Drug-Effect Predictive Treatment Optimization for Personalized Diabetes Management
Lee, E. K., Wei, X., Wright, M. D., Baker-Witt, F., & Quarshie, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-81010-7_1
On a Benchmark Problem for Automated Insulin Delivery in Type 1 Diabetes
Naik, V. V., Escorihuela‐Altaba, C., & Garcia‐Tirado, J. (2025) · Advances in industrial control · doi:10.1007/978-3-031-76312-0_7
Diagnostic approaches for detection of biomarkers in metabolic disorders
Akash, M. S. H., & Rehman, K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-36312-2.00003-6
Metabolic disorders associated with childhood obesity
Akash, M. S. H., & Rehman, K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-36312-2.00012-7
SAFETY AND EFFICACY OF PROTRACTED USAGE OF ORAL ANTIDIABETIC DRUGS IN TYPE-2 DIABETES MANAGEMENT
B., R., NAYEEM, N., Ashok Vardhan, N., & Durga, B. (2025) · Asian Journal of Pharmaceutical and Clinical Research · doi:10.22159/ajpcr.2025v18i7.55415
Personalized Meal Bolus Calculator for Type-1 Diabetes Accounting for Diurnal Effects
Krishnamoorthy, D., & Doyle, F. J. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.06.023
Validation in diabetic rats of a fully automated insulin delivery system based on impulsive offset-free MPC control
Mora, J. E. G., Arbeláez-Córdoba, N., Balcázar, N., & Rivadeneira, P. S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0330121
Competitive Catabolism in Systemic Metabolic HomeostasisPreprint
Weilandt, D., Lee, W. D., MacArthur, M. R., Liang, L., Holman, C. D., Chu, Q., Cowan, A. J., Baur, J. A., Wingreen, N. S., & Rabinowitz, J. D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.29.684184
Computer-Aided System with Machine Learning components for generating medical recommendations for type 1 diabetes patients
Nowicki, T. J. (2025) · Applied Computer Science · doi:10.35784/acs_7950
1879 Recent Developments in Smart Wearable Sensors for Real-Time Monitoring of Human Health
Patnaik, M., & Bhattacharya, N. (2025) · De Gruyter eBooks · doi:10.1515/9783111429458-009
Model reference adaptive fuzzy control of blood glucose level in patients with type 1 diabetes with input saturation
Soleimannouri, F., Farshad, M., Khorashadizadeh, S., & Mehrshad, N. (2026) · Nonlinear Dynamics · doi:10.1007/s11071-026-12328-8
An embedded system for the multiparametric analysis of biological signals : application to the pancreatic biosensor of insulin demand
Pirog, A. (2017) · theses.fr (ABES) · doi:10.70675/1e7796a6z2caaz44f1z915azf222f6adf1d6
Diabète de type 1 du modèle ... à la boucle fermée
Magdelaine, N. (2017) · doi:10.70675/abaad835z8c10z4e88z8e21z1c1b6b3604e1
Analyse de l'évolution de la glycémie des patients diabétiques insulinodépendants
Hamdi, T. (2019) · doi:10.70675/cec5d7e8za367z4da2z9069z9b6ab4e116c0
A Theoretical Study of Glucagon-Mediated Feedback in the Mammalian Circadian Clock
Liang, T., Yu, F., & Yan, J. (2026) · Mathematics · doi:10.3390/math14122199
THE ROLE AND CLINICAL SIGNIFICANCE OF CONTINUOUS GLUCOSE MONITORING METRICS İN PREDIABETES
GÜNEYSU, B. Y., & Çotuk, H. B. (2026) · AURUM Journal of Engineering Systems and Architecture · doi:10.53600/ajesa.1861999

Authors: Cobelli, C., Dalla Man, C., Sparacino, G., Magni, L., De Nicolao, G., & Kovatchev, B. P.
Affiliations: University of Padua (IT)
University of Pavia (IT)
University of Virginia (US)
ORCID: Cobelli, C.
Sparacino, G.
Magni, L.
Kovatchev, B.P.
Venue: IEEE Reviews in Biomedical Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2009
Volume: 2
Pages: 54-96
ISSN: 1937-3333 (ISSN-L)
1941-1189
DOI: 10.1109/rbme.2009.2036073
PMC: PMC2951686
OpenAlex: W2171613573
Cited by: 511
References: 304
Citations by year: 2026: 13 · 2025: 27 · 2024: 11 · 2023: 17 · 2022: 25 · 2021: 27 · 2020: 32 · 2019: 27 · 2018: 39 · 2017: 42 · 2016: 29 · 2015: 40 · 2014: 44 · 2013: 39 · 2012: 43
FWCI: 24.04
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Reviews in Biomedical Engineering (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Padua Research Archive (University of Padova) (submittedVersion)
Funders: National Institutes of Health (5r01dk051562-23)
National Institute of Diabetes and Digestive and Kidney Diseases (R01-DK-051562)
Topics: Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
Keywords: diabetes management, computer simulation, artificial pancreas, feedback control, continuous glucose monitoring, modular control architecture

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

Diabetes: Models, Signals, and Control

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks