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 accessAbstract
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.
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.
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?
The cited source says what the claim attributes to it.
In the citing paper
“in silico testing has provided directions for clinical studies and ruling out of ineffective control scenarios in a cost-effective manner. 1,2”
From The UVA/PADOVA Type 1 Diabetes Simulator: New Features (2014) · cites this paper as [1]
The cited source says what the claim attributes to it.
In the citing paper
“The development of artificial pancreas control algorithms in the past few years has been accelerated by computer simulation; for example, in silico testing has provided directions for clinical studies and ruling out of ineffective control scenarios in a cost-effective manner. 1,2”
From The UVA/PADOVA Type 1 Diabetes Simulator: New Features (2014) · cites this paper as [1]
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
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…”
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-citationBergman, 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-citationContinuous glucose monitoring and intensive treatment of type 1 diabetes
(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0805017 · Cited by 1,559
(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-”
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
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
Cited by
Sorted by citations
Items 101–200 of 509
Kovatchev, B. P. (2012) · Scientifica · doi:10.6064/2012/283821 · Cited by 34
Self-citationIntegral Backstepping Based Automated Control of Blood Glucose in Diabetes Mellitus Type 1 Patients
Babar, S. A., Ahmad, I., Arslan, M., & Zafar, M. W. (2019) · IEEE Access · doi:10.1109/access.2019.2951010 · Cited by 32
Pedersen, M. G., Cortese, G., & Eliasson, L. (2011) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2011.07.012 · Cited by 31
Blood glucose concentration control for type 1 diabetic patients: a non‐linear suboptimal approach
Batmani, Y. (2017) · IET Systems Biology · doi:10.1049/iet-syb.2016.0044 · Cited by 32
An augmented subcutaneous type 1 diabetic patient modelling and design of adaptive glucose control
Nath, A., Deb, D., & Dey, R. (2020) · Journal of Process Control · doi:10.1016/j.jprocont.2019.08.010 · Cited by 31
Kirchsteiger, H., Pölzer, S., Johansson, R., Renard, É., & del Re, L. (2011) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2011.6161344 · Cited by 31
Sankaranarayanan, S., & Fainekos, G. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33636-2_19 · Cited by 28
Towards a Verified Artificial Pancreas: Challenges and Solutions for Runtime Verification
Cameron, F., Fainekos, G., Maahs, D. M., & Sankaranarayanan, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23820-3_1 · Cited by 29
Modeling of Diabetes and Its Clinical Impact
Fritzen, K., Heinemann, L., & Schnell, O. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818785642 · Cited by 29
Wang, W., Wang, S., Wang, X., Liu, D., Geng, Y., & Wu, T. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3015199 · Cited by 31
Understanding drugs and diseases by systems biology?
Schneider, H.-C., & Klabunde, T. (2012) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2012.12.031 · Cited by 31
Zanon, M., Sparacino, G., Facchinetti, A., Riz, M., Talary, M. S., Suri, R. E., Caduff, A., & Cobelli, C. (2012) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-012-0932-6 · Cited by 29
Self-citationSupporting Content, Context and User Awareness in Future Internet Applications
Świątek, P., Juszczyszyn, K., Brzostowski, K., Drapała, J., & Grzech, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30241-1_14 · Cited by 28
Calibration algorithms for continuous glucose monitoring systems based on interstitial fluid sensing
Sun, T., Liu, J., & Chen, C. J. (2024) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2024.116450 · Cited by 28
A data driven nonlinear stochastic model for blood glucose dynamics
Zhang, Y., Holt, T., & Khovanova, N. A. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.10.021 · Cited by 28
Colmegna, P. H., Sánchez‐Peña, R., Gondhalekar, R., Dassau, E., & Doyle, F. J. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816638857 · Cited by 28
Sanz, A. J. L., Doyle, F. J., & Dassau, E. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816680632 · Cited by 28
A Minimal Model Approach for Analyzing Continuous Glucose Monitoring in Type 2 Diabetes
Goel, P., Parkhi, D., Barua, A. K., Shah, M., & Ghaskadbi, S. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00673 · Cited by 30
Improved postprandial glucose control with a customized Model Predictive Controller
Messori, M., Ellis, M. J., Cobelli, C., Christofides, P. D., & Magni, L. (2015) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2015.7172136 · Cited by 27
Self-citationZecchin, C., Facchinetti, A., Sparacino, G., Man, C. D., Manohar, C. U., Levine, J. A., Basu, A., Kudva, Y. C., & Cobelli, C. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0105 · Cited by 28
Self-citationLinear Model Identification for Personalized Prediction and Control in Diabetes
Faccioli, S., Facchinetti, A., Sparacino, G., Pillonetto, G., & Favero, S. D. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3101589 · Cited by 26
Self-citationGondhalekar, R., Dassau, E., & Doyle, F. J. (2014) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2014.6859247 · Cited by 25
GLU: a software package for analysing continuously measured glucose levels in epidemiology
Millard, L. A. C., Patel, N., Tilling, K. M., Lewcock, M., Flach, P. A., & Lawlor, D. A. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyaa004 · Cited by 26
Automatic Artificial Pancreas Systems Using an Intelligent Multiple-Model PID Strategy
Batmani, Y., Khodakaramzadeh, S., & Moradi, P. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3116376 · Cited by 26
A Model for the Estimation of Hepatic Insulin Extraction After a Meal
Piccinini, F., Man, C. D., Vella, A. V., & Cobelli, C. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2505507 · Cited by 24
“Estimating hepatic extraction of insulin (HE) becomes more complex in physiological situations, such as after meal ingestion, since in these circumstances insulin and C-peptide concentrations vary with a time course reflecting both the differences in their kinetics as well as HE [2], [3].”
Self-citationFormal Techniques for Verification and Testing of Cyber-Physical Systems
Deshmukh, J. V., & Sankaranarayanan, S. (2019) · Springer eBooks · doi:10.1007/978-3-030-13050-3_4 · Cited by 25
A Variable Reference Trajectory for Model-Free Glycemia Regulation
MohammadRidha, T., Moog, C. H., Delaleau, E., Fliess, M., & Join, C. (2015) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611974072.9 · Cited by 24
Mapping data to virtual patients in type 1 diabetes
Colmegna, P., Wang, K., Garcia-Tirado, J., & Breton, M. (2020) · Control Engineering Practice · doi:10.1016/j.conengprac.2020.104605 · Cited by 21
Real-time detection of Glucose Sensor and Insulin Pump Faults in an Artificial Pancreas.
Favero, S. D., Monaro, M., Facchinetti, A., Tagliavini, A., Sparacino, G., & Cobelli, C. (2014) · IFAC Proceedings Volumes · doi:10.3182/20140824-6-za-1003.02733 · Cited by 24
Self-citationGalvanin, F., Barolo, M., Macchietto, S., & Bezzo, F. (2010) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-010-0717-8 · Cited by 24
Optimization of insulin pump therapy based on high order run-to-run control scheme
Tuo, J., Sun, H., Shen, D., Wang, H., & Wang, Y. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.04.010 · Cited by 23
Copp, D. A., Gondhalekar, R., & Hespanha, J. P. (2017) · Optimal Control Applications and Methods · doi:10.1002/oca.2388 · Cited by 23
Robust Data-Driven Control of Artificial Pancreas Systems Using Neural Networks
Dutta, S., Kushner, T., & Sankaranarayanan, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99429-1_11 · Cited by 23
Zanon, M., Riz, M., Sparacino, G., Facchinetti, A., Suri, R. E., Talary, M. S., & Cobelli, C. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090702 · Cited by 23
Self-citationBrahim, N. B., Place, J., Renard, É., & Breton, M. D. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815607864 · Cited by 23
Farman, M. T., Hasan, A., Xu, C., Nisar, K. S., & Hınçal, E. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108420 · Cited by 22
The contribution of glucagon in an Artificial Pancreas for people with type 1 diabetes
Bátora, V., Tárník, M., Murgaš, J., Schmidt, S., Nørgaard, K., Poulsen, N. K., Madsen, H., Boiroux, D., & Jørgensen, J. B. (2015) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2015.7172134 · Cited by 22
Personalized LSTM-based alarm systems for hypoglycemia and hyperglycemia prevention
Iacono, F. D., Magni, L., & Toffanin, C. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105167 · Cited by 24
Self-citationUnderstanding the Metabolic Syndrome: A Modeling Perspective
Khoo, M. C. K., Oliveira, F., & Cheng, L. (2012) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2012.2232651 · Cited by 24
Faust, O., Yu, W., & Acharya, U. R. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.12.024 · Cited by 22
Closed-loop control of blood glucose level in type-1 diabetics: A simulation study
Soylu, S., Danışman, K., Saçu, İ. E., & Alçı, M. (2013) · National Conference on Electrical and Electronics Engineering (ELECO) · doi:10.1109/eleco.2013.6713864 · Cited by 21
Micheletto, F., Man, C. D., Kolterman, O., Chiquette, E., Herrmann, K., Schirra, J., Kovatchev, B., & Cobelli, C. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0054 · Cited by 24
Self-citationUsing meta-differential evolution to enhance a calculation of a continuous blood glucose level
Koutný, T. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.05.011 · Cited by 21
Blood glucose concentration control for type 1 diabetic patients: a multiple‐model strategy
Batmani, Y., & Khodakaramzadeh, S. (2019) · IET Systems Biology · doi:10.1049/iet-syb.2018.5049 · Cited by 21
An adaptive technique based blood glucose control in type‐1 diabetes mellitus patients
Belmon, A. P., & Auxillia, J. (2020) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.3371 · Cited by 21
Fractional Order PID Controller for Diabetes Patients
Goharimanesh, M., Lashkaripour, A., & Mehrizi, A. A. (2015) · doi:10.22059/jcamech.2015.53395 · Cited by 21
Yu, X., Yang, Z., Sun, X., Liu, H., Li, H., Lu, J., Zhou, J. H., & Çınar, A. (2025) · AI · doi:10.3390/ai6050087 · Cited by 23
Rodríguez‐Rodríguez, I., Rodríguez, J., González-Vidal, A., & Zamora, M. Á. (2019) · Symmetry · doi:10.3390/sym11091164 · Cited by 20
Contreras, S., Medina-Ortiz, D., Conca, C., & Olivera-Nappa, Á. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00195 · Cited by 15
Cobelli, C., & Man, C. D. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211015268 · Cited by 22
Self-citationPhillips, N. E., Collet, T., & Naef, F. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100545 · Cited by 21
Inzucchi, S. E., Umpierrez, G. E., DIGENIO, A. G., Zhou, R., & Kovatchev, B. P. (2015) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2014.12.020 · Cited by 20
Self-citationJain, P., Joshi, A. M., & Mohanty, S. P. (2020) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot48130.2020.9221132 · Cited by 20
Li, P., Yu, L., Fang, Q., & Lee, S.-Y. (2015) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-015-1436-y · Cited by 20
An adaptive back-stepping control for blood glucose regulation in type 1 diabetes
Sepasi, S., Kalat, A. A., & Seyedabadi, M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102498 · Cited by 19
Artificial Intelligence in Efficient Diabetes Care
Khodve, G., & Banerjee, S. (2022) · Current Diabetes Reviews · doi:10.2174/1573399819666220905163940 · Cited by 19
García-Jaramillo, M., Calm, R., Bondía, J., & Vehı́, J. (2012) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2012.04.003 · Cited by 19
Sadikoglu, F. M., & Kavalcıoğlu, C. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.09.410 · Cited by 18
Koutný, T. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.08.008 · Cited by 18
Messori, M., Cobelli, C., & Magni, L. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.09.148 · Cited by 18
Self-citationBoiroux, D., Hagdrup, M., Mahmoudi, Z., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2016) · European Control Conference (ECC) · doi:10.1109/ecc.2016.7810604 · Cited by 18
Fabris, C., Özaslan, B., & Breton, M. D. (2019) · Sensors · doi:10.3390/s19245386 · Cited by 19
Yang, J. F., Gong, X., Bakh, N. A., Carr, K. M., Phillips, N. F. B., Ismail‐Beigi, F., Weiss, M. A., & Strano, M. S. (2020) · Diabetes · doi:10.2337/db19-0879 · Cited by 19
Sliding‐mode‐based controllers for automation of blood glucose concentration for type 1 diabetes
Babar, S. A., Ahmad, I., & Mughal, I. S. (2021) · IET Systems Biology · doi:10.1049/syb2.12015 · Cited by 22
Ahdab, M. A., Leth, J., Knudsen, T., Vestergaard, P., & Clausen, H. G. (2021) · Biochemical Engineering Journal · doi:10.1016/j.bej.2021.108170 · Cited by 18
Nonlinear gain in online prediction of blood glucose profile in type 1 diabetic patients
Estrada, G. C., del Re, L., & Renard, E. (2010) · IEEE Conference on Decision and Control · doi:10.1109/cdc.2010.5717390 · Cited by 20
Mathematical Modeling of the Blood Glucose Regulation System in Diabetes Mellitus Patients
Александрович, К. В., Philippov, Y. I., Tarasov, Y. V., Boyarsky, M. D., Mayorov, A. Y., Shestakova, M. V., & Dedov, I. I. (2015) · Annals of the Russian academy of medical sciences · doi:10.15690/vramn.v70.i5.1441 · Cited by 18
Nonlinear Model Predictive Control for an Artificial β-cell
Boiroux, D., Finan, D. A., Jørgensen, J. B., Poulsen, N. K., & Madsen, H. (2010) · Springer eBooks · doi:10.1007/978-3-642-12598-0_26 · Cited by 17
Sensor-Augmented Pump-Based Customized Mathematical Model for Type 1 Diabetes
Grosman, B., Wu, D., Miller, D. M., LINTEREUR, L. J., Roy, A., Parikh, N., & KAUFMAN, F. R. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0333 · Cited by 18
Nonlinear system identification via data augmentation
Formentin, S., Mazzoleni, M., Scandella, M., & Previdi, F. (2019) · Systems & Control Letters · doi:10.1016/j.sysconle.2019.04.004 · Cited by 17
Babar, S. A., Ahmad, I., Mughal, I. S., & Khan, S. A. (2021) · IEEE Access · doi:10.1109/access.2021.3057365 · Cited by 19
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. (2020) · IEEE Control Systems Letters · doi:10.1109/lcsys.2020.3047493 · Cited by 17
Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis
Hoyos, J. D., Villa-Tamayo, M. F., Builes-Montaño, C., Ramírez-Rincón, A., Godoy, J. L., García-Tirado, J., & Rivadeneira, P. (2021) · IEEE Access · doi:10.1109/access.2021.3076405 · Cited by 13
Linear Modeling and Prediction in Diabetes Physiology
Cescon, M., & Johansson, R. (2014) · Lecture notes in bioengineering · doi:10.1007/978-3-642-54464-4_9 · Cited by 23
Abbes, I. B., Richard, P., Lefebvre, M.-A., Guilhem, I., & Poirier, J. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700315 · Cited by 12
A Novel Classification Indicator of Type 1 and Type 2 Diabetes in China
Wang, Y., Liu, S., Chen, R., Chen, Z., Yuan, J., & Li, Q. (2017) · Scientific Reports · doi:10.1038/s41598-017-17433-8 · Cited by 17
Asadi, S., & Nekoukar, V. (2018) · Mathematical Biosciences · doi:10.1016/j.mbs.2018.09.006 · Cited by 17
Diabetes Technology: Monitoring, Analytics, and Optimal ControlPreprint
Kovatchev, B. P. (2018) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a034389 · Cited by 17
Self-citationModels, Devices, Properties, and Verification of Artificial Pancreas Systems
Kushner, T., Bequette, B. W., Cameron, F. M., Forlenza, G. P., Maahs, D. M., & Sankaranarayanan, S. (2019) · Computational biology · doi:10.1007/978-3-030-17297-8_4 · Cited by 18
Facchinetti, A., Favero, S. D., Sparacino, G., & Cobelli, C. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091226 · Cited by 16
Self-citationMechanisms Underlying the Pathogenesis of Isolated Impaired Glucose Tolerance in Humans
Varghese, R. T., Man, C. D., Sharma, A. R., Viegas, I., Barosa, C., Marques, C., Shah, M., Miles, J. M., Rizza, R. A., Jones, J. G., et al. (2016) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2016-1998 · Cited by 16
Self-citationTowards a Model-Based Meal Detector for Type I Diabetics
Chen, S., Weimer, J. E., & Rickels, M. R. (2015) · ScholarlyCommons (University of Pennsylvania) · Cited by 16
Outcome-Driven Personalized Treatment Design for Managing Diabetes
Lee, E. K., Wei, X., Baker-Witt, F., Wright, M. D., & Quarshie, A. (2018) · INFORMS Journal on Applied Analytics · doi:10.1287/inte.2018.0964 · Cited by 16
Pavan, J., Salvagnin, D., Facchinetti, A., Sparacino, G., & Favero, S. D. (2022) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2022.3195072 · Cited by 15
Self-citationLinking abstract analysis to concrete design: A hierarchical approach to verify medical CPS safety
Murugesan, A., Sokolsky, O. V., Rayadurgam, S., Whalen, M. W., Heimdahl, M. P. E., & Lee, I. (2014) · ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) · doi:10.1109/iccps.2014.6843718 · Cited by 15
Analysis and modelling of glucose metabolism in diabetic Göttingen minipigs
Lunze, K., Woitok, A., Walter, M., Brendel, M. D., Afify, M., Tolba, R. H., & Leonhardt, S. K. (2014) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2014.04.003 · Cited by 15
LMI-Based Approaches for the Calibration of Continuous Glucose Measurement Sensors
Kirchsteiger, H., Zaccarian, L., Renard, É., & del Re, L. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2341703 · Cited by 15
Designing an artificial pancreas architecture: the AP@home experience
Lanzola, G., Toffanin, C., Palma, F. D., Favero, S. D., Magni, L., & Bellazzi, R. (2014) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-014-1231-1 · Cited by 15
Self-citationA model-based approach to synthesizing insulin infusion pump usage parameters for diabetic patients
Sankaranarayanan, S., Miller, C., Raghunathan, R., Ravanbakhsh, H., & Fainekos, G. (2012) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2012.6483413 · Cited by 15
Grosman, B., Wu, D., Parikh, N., Roy, A., Voskanyan, G. R., Kurtz, N., Sturis, J., Cohen, O., Ekelund, M., & Vigersky, R. A. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106087 · Cited by 17
Classification of Factors Affecting Blood Glucose Concentration Dynamics
Bazaev, N. A., Pletenev, A. N., & Витольдович, П. К. (2013) · Biomedical Engineering · doi:10.1007/s10527-013-9344-7 · Cited by 15
Vella, A. V., Sathananthan, M., Ikramuddin, S., Swain, J. M., Shah, M., Piccinini, F., Man, C. D., Cobelli, C., Rizza, R. A., & Camilleri, M. (2014) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s65733 · Cited by 15
Self-citationVarghese, R. T., Man, C. D., LAURENTI, M. C., Piccinini, F., Sharma, A. R., Shah, M., Bailey, K. R., Rizza, R. A., Cobelli, C., & Vella, A. V. (2017) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13106 · Cited by 15
Self-citationParameter estimation of a meal glucose–insulin model for TIDM patients from therapy historical data
Sánchez, O. D., Ruiz-Velazquez, E., Alanís, A. Y., Quiroz, G., & Torres‐Treviño, L. (2018) · IET Systems Biology · doi:10.1049/iet-syb.2018.5038 · Cited by 16
Individual categorisation of glucose profiles using compositional data analysis
Biagi, L., Bertachi, A. H., Giménez, M., Conget, I., Bondía, J., Fernández, J. A. M., & Vehı́, J. (2018) · Statistical Methods in Medical Research · doi:10.1177/0962280218808819 · Cited by 15
Bihormonal model predictive control of blood glucose in people with type 1 diabetes
Bátora, V., Tárník, M., Murgaš, J., Schmidt, S., Nørgaard, K., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2014) · The proceedings of the IEEE Conference on Control Applications/The proceedings of the ... IEEE Conference on Control Applications · doi:10.1109/cca.2014.6981556 · Cited by 14
Challenges and progress in the development of a closed-loop artificial pancreas
Bequette, B. W. (2012) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2012.6315593 · Cited by 14
Diabetes Monitoring System Using Mobile Computing Technologies
Saud, M. A., & Abdullah, M. (2013) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2013.040204 · Cited by 14
Cuesta–Frau, D., Novák, D., Burda, V., Molina, Á., Vargas, B., Mráz, M., Kaválková, P., Beneš, M., & Haluzı́k, M. (2018) · Entropy · doi:10.3390/e20110871 · Cited by 14
Development platform for artificial pancreas algorithms
Smaoui, M. R., RABASA-LHORET, R. P. R., & Haidar, A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243139 · Cited by 16
Metadata
- 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)
- Venue
- IEEE Reviews in Biomedical Engineering
- Published
- 2009
- Volume
- 2
- Pages
- 54-96
- ISSN
- 1937-3333 (ISSN-L)
- 1941-1189
- 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
- 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