Modeling the glucose sensor error

Continuous glucose monitoring and intensive treatment of type 1 diabetes

(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0805017 · Cited by 1,559 · see citing works

<i>In Silico</i> Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes

Kovatchev, B. P., Breton, M., Dalla Man, C., & Cobelli, C. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300106 · Cited by 760 · see citing works

Artificial Pancreas: Past, Present, Future

Cobelli, C., Renard, E., & Kovatchev, B. (2011) · Diabetes · doi:10.2337/db11-0654 · Cited by 600 · see citing works

Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial

Hovorka, R., Allen, J. M., Elleri, D., Chassin, L. J., Harris, J. R., Xing, D., Kollman, C., Hovorka, T., Larsen, A. M. F., Nodale, M., et al. (2010) · The Lancet · doi:10.1016/s0140-6736(09)61998-x · Cited by 448 · see citing works

Nocturnal Glucose Control with an Artificial Pancreas at a Diabetes Camp

Phillip, M., Battelino, T., Atlas, E., Kordonouri, O., Bratina, N., Miller, S., Biester, T., Stefanija, M. A., Muller, I., Nimri, R., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1206881 · Cited by 431 · see citing works

Subcutaneous glucose predicts plasma glucose independent of insulin: implications for continuous monitoring

Rebrin, K., Steil, G. M., van Antwerp, W. P., & Mastrototaro, J. J. (1999) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1999.277.3.e561 · Cited by 367 · see citing works

Fully Integrated Artificial Pancreas in Type 1 Diabetes

Breton, M., Farret, A., Bruttomesso, D., Anderson, S., Magni, L., Patek, S., Dalla Man, C., Place, J., Demartini, S., Del Favero, S., et al. (2012) · Diabetes · doi:10.2337/db11-1445 · Cited by 315 · see citing works

Symmetrization of the Blood Glucose Measurement Scale and Its Applications

Kovatchev, B. P., Cox, D. J., Gonder-Frederick, L. A., & Clarke, W. L. (1997) · Diabetes Care · doi:10.2337/diacare.20.11.1655 · Cited by 258 · see citing works

Comparison of the Numerical and Clinical Accuracy of Four Continuous Glucose Monitors

Kovatchev, B. P., Anderson, S. M., Heinemann, L., & Clarke, W. L. (2008) · Diabetes Care · doi:10.2337/dc07-2401 · Cited by 236 · see citing works

Glucose Variability

Service, F. J. (2013) · Diabetes · doi:10.2337/db12-1396 · Cited by 222 · see citing works

Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms

Bequette, B. W. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400222 · Cited by 193 · see citing works

Blood Glucose Control in Type 1 Diabetes With a Bihormonal Bionic Endocrine Pancreas

Russell, S. J., El-Khatib, F. H., Nathan, D. M., Magyar, K. L., Jiang, J., & Damiano, E. R. (2012) · Diabetes Care · doi:10.2337/dc12-0071 · Cited by 176 · see citing works

Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors

Breton, M. D., & Kovatchev, B. P. (2008) · Journal of Diabetes Science and Technology · doi:10.1177/193229680800200517 · Cited by 173 · see citing works

Pilot Studies of Wearable Outpatient Artificial Pancreas in Type 1 Diabetes

Cobelli, C., Renard, É., Kovatchev, B. P., Keith-Hynes, P., Brahim, N. B., Place, J., Favero, S. D., Breton, M. D., Farret, A., Bruttomesso, D., et al. (2012) · Diabetes Care · doi:10.2337/dc12-0660 · Cited by 119 · see citing works

A Comparative Effectiveness Analysis of Three Continuous Glucose Monitors

Damiano, E. R., El-Khatib, F. H., Zheng, H., Nathan, D. M., & Russell, S. J. (2012) · Diabetes Care · doi:10.2337/dc12-0070 · Cited by 109 · see citing works

Real-Time Improvement of Continuous Glucose Monitoring Accuracy

Facchinetti, A., Sparacino, G., Guerra, S., Luijf, Y. M., DeVries, J. H., Mader, J. K., Ellmerer, M., Benesch, C., Heinemann, L., Bruttomesso, D., et al. (2012) · Diabetes Care · doi:10.2337/dc12-0736 · Cited by 106 · see citing works

“Smart” Continuous Glucose Monitoring Sensors: On-Line Signal Processing Issues

Sparacino, G., Facchinetti, A., & Cobelli, C. (2010) · Sensors · doi:10.3390/s100706751 · Cited by 95 · see citing works

Enhanced Accuracy of Continuous Glucose Monitoring by Online Extended Kalman Filtering

Facchinetti, A., Sparacino, G., & Cobelli, C. (2010) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2009.0158 · Cited by 70 · see citing works

Personal continuous glucose monitoring (CGM) in diabetes management: Review of the literature and implementation for practical use

Joubert, M., & Reznik, Y. (2012) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2011.12.010 · Cited by 70 · see citing works

Analysis of Time Lags and Other Sources of Error of the DexCom SEVEN Continuous Glucose Monitor

Kamath, A., Mahalingam, A., & Brauker, J. H. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2009.0060 · Cited by 69 · see citing works

Modeling the Error of Continuous Glucose Monitoring Sensor Data: Critical Aspects Discussed through Simulation Studies

Facchinetti, A., Sparacino, G., & Cobelli, C. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400102 · Cited by 67 · see citing works

Detection of Hypoglycemia with Continuous Interstitial and Traditional Blood Glucose Monitoring Using the FreeStyle Navigator Continuous Glucose Monitoring System

McGarraugh, G. V., & Bergenstal, R. M. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0047 · Cited by 61 · see citing works

Enhancing the Accuracy of Subcutaneous Glucose Sensors: A Real-Time Deconvolution-Based Approach

Guerra, S., Facchinetti, A., Sparacino, G., Nicolao, G. D., & Cobelli, C. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2191782 · Cited by 61 · see citing works

Online Denoising Method to Handle Intraindividual Variability of Signal-to-Noise Ratio in Continuous Glucose Monitoring

Facchinetti, A., Sparacino, G., & Cobelli, C. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2161083 · Cited by 60 · see citing works

Glycemic Variability: Measurement and Utility in Clinical Medicine and Research—One Viewpoint

Rodbard, D. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0104 · Cited by 54 · see citing works

Continuous glucose monitors

Lane, J. E., Shivers, J. P., & Zisser, H. (2013) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0b013e32835edb9d · Cited by 50 · see citing works

Reduction of Number and Duration of Hypoglycemic Events by Glucose Prediction Methods: A Proof-of-Concept In Silico Study

Zecchin, C., Facchinetti, A., Sparacino, G., & Cobelli, C. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2012.0208 · Cited by 47 · see citing works

A Dynamic Risk Measure from Continuous Glucose Monitoring Data

Guerra, S., Sparacino, G., Facchinetti, A., Schiavon, M., Man, C. D., & Cobelli, C. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0006 · Cited by 37 · see citing works

Fitting dynamic models with forcing functions: Application to continuous glucose monitoring in insulin therapy

Lunn, D. J., Wei, C., & Hovorka, R. (2011) · Statistics in Medicine · doi:10.1002/sim.4254 · Cited by 34 · see citing works

The Accuracy Benefit of Multiple Amperometric Glucose Sensors in People With Type 1 Diabetes

Castle, J. R., Pitts, A., Hanavan, K., Muhly, R., Youssef, J. E., Karvetski, C., Kovatchev, B. P., & Ward, W. K. (2012) · Diabetes Care · doi:10.2337/dc11-1929 · Cited by 30 · see citing works

An Overview of Continuous Glucose Monitoring Systems

Cox, M. A. (2009) · Journal of Pediatric Health Care · doi:10.1016/j.pedhc.2009.06.002 · Cited by 25 · see citing works

Methods of Evaluating the Utility of Continuous Glucose Monitor Alerts

Kamath, A., Mahalingam, A., & Brauker, J. H. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400108 · Cited by 12 · see citing works

Mobile technology and the digitization of healthcare

Bhavnani, S. P., Narula, J., & Sengupta, P. P. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehv770 · Cited by 460 · see citing works

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Chase, J., Preiser, J., Dickson, J. L., Pironet, A., Chiew, Y., Pretty, C., Shaw, G., Benyó, B., Moeller, K., Safaei, S., et al. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0455-y · Cited by 169 · see citing works

Concentration Measurement of Microliter-Volume Water–Glucose Solutions Using $Q$ Factor of Microwave Sensors

Juan, C. G., Bronchalo, E., Potelon, B., Quendo, C., Ávila-Navarro, E., & Sabater-Navarro, J. M. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2866743 · Cited by 177 · see citing works

Continuous glucose monitoring sensors: past, present and future algorithmic challenges

Facchinetti, A. (2016) · Sensors · doi:10.3390/s16122093 · Cited by 176 · see citing works

The Artificial Pancreas: How Closed-Loop Control Is Revolutionizing Diabetes

Haidar, A. (2016) · IEEE Control Systems · doi:10.1109/mcs.2016.2584318 · Cited by 137 · see citing works

Artificial Pancreas: Model Predictive Control Design from Clinical Experience

Toffanin, C., Messori, M., Palma, F. D., Nicolao, G. D., Cobelli, C., & Magni, L. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700607 · Cited by 106 · see citing works

Continuous glucose monitoring in neonates: a review

McKinlay, C. J. D., Chase, J. G., Dickson, J. L., Harris, D. L., Alsweiler, J. M., & Harding, J. E. (2017) · Maternal Health Neonatology and Perinatology · doi:10.1186/s40748-017-0055-z · Cited by 87 · see citing works

Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices

Facchinetti, A., Del Favero, S., Sparacino, G., & Cobelli, C. (2015) · Medical &amp; Biological Engineering &amp; Computing · doi:10.1007/s11517-014-1226-y · Cited by 80 · see citing works

A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model

Resalat, N., El Youssef, J., Tyler, N., Castle, J., & Jacobs, P. G. (2019) · PLOS ONE · doi:10.1371/journal.pone.0217301 · Cited by 78 · see citing works

Adaptive control in an artificial pancreas for people with type 1 diabetes

Boiroux, D., Duun‐Henriksen, A. K., Schmidt, S., Nørgaard, K., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2016) · Control Engineering Practice · doi:10.1016/j.conengprac.2016.01.003 · Cited by 78 · see citing works

A deep learning approach to adherence detection for type 2 diabetics

Mohebbi, A., Aradóttir, T. B., Johansen, A. R., Bengtsson, H. L., Fraccaro, M., & Mørup, M. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037462 · Cited by 72 · see citing works

Untangling glycaemia and mortality in critical care

Uyttendaele, V., Dickson, J. L., Shaw, G. M., Desaive, T., & Chase, J. G. (2017) · Critical Care · doi:10.1186/s13054-017-1725-y · Cited by 70 · see citing works

Data-Driven Anomaly Recognition for Unsupervised Model-Free Fault Detection in Artificial Pancreas

Meneghetti, L., Terzi, M., Favero, S. D., Susto, G. A., & Cobelli, C. (2018) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2018.2885963 · Cited by 68 · see citing works

Prediction of Adverse Glycemic Events From Continuous Glucose Monitoring Signal

Gadaleta, M., Facchinetti, A., Grisan, E., & Rossi, M. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2823763 · Cited by 67 · see citing works

Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator—in silico evaluation under intra-day variability

Herrero, P., Bondia, J., Adewuyi, O., Pesl, P., El-Sharkawy, M., Reddy, M., Toumazou, C., Oliver, N., & Georgiou, P. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.05.010 · Cited by 63 · see citing works

Glucose Concentration Measurement in Human Blood Plasma Solutions with Microwave Sensors

Juan, C. G., Bronchalo, E., Potelon, B., Quendo, C., & Sabater-Navarro, J. M. (2019) · Sensors · doi:10.3390/s19173779 · Cited by 63 · see citing works

Modeling Transient Disconnections and Compression Artifacts of Continuous Glucose Sensors

Facchinetti, A., Favero, S. D., Sparacino, G., & Cobelli, C. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0250 · Cited by 59 · see citing works

Accuracy of Continuous Glucose Monitoring Measurements in Normo-Glycemic Individuals

Akintola, A. A., Noordam, R., Jansen, S. W. M., de Craen, A. J. M., Ballieux, B. E. P. B., Cobbaert, C. M., Mooijaart, S. P., Pijl, H., Westendorp, R. G. J., & van Heemst, D. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139973 · Cited by 58 · see citing works

Improving Accuracy and Precision of Glucose Sensor Profiles: Retrospective Fitting by Constrained Deconvolution

Favero, S. D., Facchinetti, A., Sparacino, G., & Cobelli, C. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2293531 · Cited by 55 · see citing works

Overnight glucose control in people with type 1 diabetes

Boiroux, D., Duun‐Henriksen, A. K., Schmidt, S., Nørgaard, K., Madsbad, S., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.08.005 · Cited by 52 · see citing works

Fault and meal detection by redundant continuous glucose monitors and the unscented Kalman filter

Mahmoudi, Z., Nørgaard, K., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.05.004 · Cited by 46 · see citing works

Glucose Sensor Dynamics and the Artificial Pancreas: The Impact of Lag on Sensor Measurement and Controller Performance

Huyett, L., Dassau, E., Zisser, H., & Doyle, F. J. (2018) · IEEE Control Systems · doi:10.1109/mcs.2017.2766322 · Cited by 39 · see citing works

Limits to the Evaluation of the Accuracy of Continuous Glucose Monitoring Systems by Clinical Trials

Schrangl, P., Reiterer, F., Heinemann, L., Freckmann, G., & Del Re, L. (2018) · Biosensors · doi:10.3390/bios8020050 · Cited by 46 · see citing works

Glycemic control in the intensive care unit: A control systems perspective

Chase, J., Benyó, B., & Desaive, T. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.03.007 · Cited by 39 · see citing works

Rapid Model Identification for Online Subcutaneous Glucose Concentration Prediction for New Subjects With Type I Diabetes

Zhao, C., & Yu, C. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2387293 · Cited by 44 · see citing works

A Review of Safety and Hazards Associated With the Artificial Pancreas

Ramkissoon, C. M., Aufderheide, B., Bequette, B. W., & Vehı́, J. (2017) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2017.2749038 · Cited by 41 · see citing works

Dielectric characterization of water glucose solutions using a transmission/reflection line method

Juan, C. G., Bronchalo, E., Torregrosa‐Penalva, G., Ávila-Navarro, E., García-Aracil, N., & Sabater-Navarro, J. M. (2016) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2016.07.011 · Cited by 38 · see citing works

A comprehensive review of models and nonlinear control strategies for blood glucose regulation in artificial pancreas

Mughal, I. S., Patané, L., & Caponetto, R. (2024) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2024.100937 · Cited by 37 · see citing works

Feasibility study of portable microwave microstrip open-loop resonator for non-invasive blood glucose level sensing: proof of concept

Juan, C. G., García, H. G., Ávila-Navarro, E., Bronchalo, E., Galiano, V., Moreno, Ó., Orozco‐Beltrán, D., & Sabater-Navarro, J. M. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-02030-w · Cited by 36 · see citing works

An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes

Boiroux, D., Aradóttir, T. B., Nørgaard, K., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816666295 · Cited by 34 · see citing works

Control of Diabetes Mellitus by Advanced Robust Control Solution

Kovács, L., Eigner, G., Siket, M., & Barkai, L. (2019) · IEEE Access · doi:10.1109/access.2019.2938267 · Cited by 33 · see citing works

Model Identification using Continuous Glucose Monitoring Data for Type 1 Diabetes

Boiroux, D., Hagdrup, M., Mahmoudi, Z., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.07.279 · Cited by 31 · see citing works

Accuracy and reliability of a subcutaneous continuous glucose monitoring device in critically ill patients

Rijkenberg, S., van Steen, S. C. J., DeVries, J. H., & van der Voort, P. H. J. (2017) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-017-0086-z · Cited by 31 · see citing works

Differences Between Flash Glucose Monitor and Fingerprick Measurements

Staal, O. M., Hansen, H. M. U., Christiansen, S. C., Fougner, A. L., Carlsen, S. M., & Stavdahl, Ø. (2018) · Biosensors · doi:10.3390/bios8040093 · Cited by 31 · see citing works

Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime

Vettoretti, M., Battocchio, C., Sparacino, G., & Facchinetti, A. (2019) · Sensors · doi:10.3390/s19235320 · Cited by 31 · see citing works

In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting

DeJournett, L., & DeJournett, J. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816653967 · Cited by 31 · see citing works

Auto-Regressive Time Delayed jump neural network for blood glucose levels forecasting

D’Antoni, F., Merone, M., Piemonte, V., Iannello, G., & Soda, P. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106134 · Cited by 29 · see citing works

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 · see citing works

Comparison Between One-Point Calibration and Two-Point Calibration Approaches in a Continuous Glucose Monitoring Algorithm

Mahmoudi, Z., Johansen, M. D., Christiansen, J. S., & Hejlesen, O. K. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814531356 · Cited by 28 · see citing works

Continuous Glucose Monitoring Measures Can Be Used for Glycemic Control in the ICU: An In-Silico Study

Zhou, T., Dickson, J. L., Shaw, G. M., & Chase, J. G. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817738791 · Cited by 26 · see citing works

Long-term use of the hybrid artificial pancreas by adjusting carbohydrate ratios and programmed basal rate: A reinforcement learning approach

Jafar, A., Fathi, A. E., & Haidar, A. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.105936 · Cited by 25 · see citing works

Online Calibration of Glucose Sensors From the Measured Current by a Time-Varying Calibration Function and Bayesian Priors

Vettoretti, M., Facchinetti, A., Favero, S. D., Sparacino, G., & Cobelli, C. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2426217 · Cited by 25 · see citing works

Effect of BGM Accuracy on the Clinical Performance of CGM: An In-Silico Study

Campos-Náñez, E., & Breton, M. D. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817710476 · Cited by 24 · see citing works

Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor

Biagi, L., Ramkissoon, C. M., Facchinetti, A., Leal, Y., & Vehı́, J. (2017) · Sensors · doi:10.3390/s17061361 · Cited by 24 · see citing works

Towards Safe and Robust Closed-Loop Artificial Pancreas Using Improved PID-Based Control Strategies

Alshalalfah, A.-L., Hamad, G. B., & Mohamed, O. A. (2021) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2021.3058355 · Cited by 24 · see citing works

Clinical impact of blood glucose monitoring accuracy: an in-silico study

Campos-Náñez, E., Fortwaengler, K., & Breton, M. D. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817710474 · Cited by 22 · see citing works

A real-time continuous glucose monitoring-based algorithm to trigger hypotreatments to prevent/mitigate hypoglycemic events

Camerlingo, N., Vettoretti, M., Del Favero, S., Cappon, G., Sparacino, G., & Facchinetti, A. (2019) · Diabetes Technology &amp; Therapeutics · doi:10.1089/dia.2019.0139 · Cited by 22 · see citing works

A Model-Based Insulin Dose Optimization Algorithm for People With Type 1 Diabetes on Multiple Daily Injections Therapy

Fathi, A. E., Kearney, R., Palisaitis, E., Boulet, B., & Haidar, A. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3023555 · Cited by 20 · see citing works

Accuracy Evaluation of a New Real-Time Continuous Glucose Monitoring Algorithm in Hypoglycemia

Mahmoudi, Z., Jensen, M. H., Johansen, M. D., Christensen, T. F., Tarnow, L., Christiansen, J. S., & Hejlesen, O. K. (2014) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2014.0043 · Cited by 20 · see citing works

Machine Learning-Based Adherence Detection of Type 2 Diabetes Patients on Once-Daily Basal Insulin Injections

Thyde, D. N., Mohebbi, A., Bengtsson, H. L., Jensen, M. L., & Mørup, M. (2020) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820912411 · Cited by 20 · see citing works

A Model of Self-Monitoring Blood Glucose Measurement Error

Vettoretti, M., Facchinetti, A., Sparacino, G., & Cobelli, C. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817698498 · Cited by 18 · see citing works

An ensemble nonlinear model predictive control algorithm in an artificial pancreas for people with type 1 diabetes

Boiroux, 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 · see citing works

Glucose Sensing in the Subcutaneous Tissue: Attempting to Correlate the Immune Response with Continuous Glucose Monitoring Accuracy

Joseph, J. I., EISLER, G. J., Diaz, D., Khalf, A., Loeum, C., & Torjman, M. C. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0106 · Cited by 18 · see citing works

Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems

Zhou, X., Ahmed, B., Aylor, J. H., Asare, P., & Alemzadeh, H. (2021) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn48987.2021.00058 · Cited by 15 · see citing works

Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor

Zhou, T., Dickson, J. L., & Chase, J. G. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817719089 · Cited by 15 · see citing works

Exploring Robustness in Blood Glucose Control with Unannounced Meal Intake for Type-1 Diabetes Patient

Szalay, P., Drexler, D. A., & Kovács, L. (2023) · Acta Polytechnica Hungarica · doi:10.12700/aph.20.8.2023.8.3 · Cited by 14 · see citing works

Comparing the accuracy of transcutaneous sensor and 90-day implantable glucose sensor

Boscari, F., Vettoretti, M., Amato, A. M. L., Vallone, V., Uliana, A., Iori, E., Avogaro, A., Facchinetti, A., & Bruttomesso, D. (2020) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2020.09.006 · Cited by 14 · see citing works

Retrofitting of Continuous Glucose Monitoring Traces Allows More Accurate Assessment of Glucose Control in Outpatient Studies

Favero, S. D., Facchinetti, A., Sparacino, G., & Cobelli, C. (2015) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2014.0230 · Cited by 14 · see citing works

Parameter Estimation in Type 1 Diabetes Models for Model-Based Control Applications

Boiroux, D., Mahmoudi, Z., & Jørgensen, J. B. (2019) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2019.8814933 · Cited by 14 · see citing works

Modeling the error of factory-calibrated continuous glucose monitoring sensors: application to Dexcom G6 sensor data

Vettoretti, M., Favero, S. D., Sparacino, G., & Facchinetti, A. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856790 · Cited by 14 · see citing works

Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas

Reenberg, A. T., Ritschel, T. K. S., Lindkvist, E. B., Laugesen, C., Svensson, J., Ranjan, A. G., Nørgaard, K., & Jørgensen, J. B. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.07.561 · Cited by 13 · see citing works

Electrochemical Glucose Biosensors for Diabetes Care

Ocvirk, G., Buck, H., & DuVall, S. H. (2016) · Bioanalytical reviews · doi:10.1007/11663_2016_3 · Cited by 13 · see citing works

Using uncertain data from body-worn sensors to gain insight into type 1 diabetes

Heintzman, N. D., & Kleinberg, S. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.08.022 · Cited by 12 · see citing works

Comparison of three nonlinear filters for fault detection in continuous glucose monitors

Mahmoudi, Z., Wendt, S. L., Boiroux, D., Hagdrup, M., Nørgaard, K., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591484 · Cited by 12 · see citing works

Application of the continuous-discrete extended Kalman filter for fault detection in continuous glucose monitors for type 1 diabetes

Mahmoudi, Z., Boiroux, D., Hagdrup, M., Nørgaard, K., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2016) · European Control Conference (ECC) · doi:10.1109/ecc.2016.7810373 · Cited by 12 · see citing works

Nonlinear Model Predictive Control and Artificial Pancreas Technologies

Boiroux, D., & Jørgensen, J. B. (2018) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2018.8619521 · Cited by 12 · see citing works

Bayesian denoising algorithm dealing with colored, non-stationary noise in continuous glucose monitoring timeseries

Camerlingo, N., Siviero, I., Vettoretti, M., Sparacino, G., Favero, S. D., & Facchinetti, A. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1280233 · Cited by 12 · see citing works

Advanced decision support system for individuals with diabetes on multiple daily injections therapy using reinforcement learning and nearest-neighbors: In-silico and clinical results

Jafar, A., Pasqua, M., Olson, B., & Haidar, A. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102749 · Cited by 11 · see citing works

Fault Detection in Artificial Pancreas: A Model-Free approach

Meneghetti, L., Terzi, M., Susto, G. A., Favero, S. D., & Cobelli, C. (2018) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2018.8619048 · Cited by 11 · see citing works

A Bolus Calculator Based on Continuous-Discrete Unscented Kalman Filtering for Type 1 Diabetics∗∗Funded by the Danish Diabetes Academy supported by the Novo Nordisk Foundation. Contact information: John Bagterp Jørgensen (jbjo@dtu.dk).

Boiroux, D., Aradóttir, T. B., Hagdrup, M., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.10.132 · Cited by 11 · see citing works

A Nonlinear Model Predictive Control Strategy for Glucose Control in People with Type 1 Diabetes

Boiroux, D., & Jørgensen, J. B. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.11.644 · Cited by 11 · see citing works

Design of a Real-time Self-adjusting Calibration Algorithm to Improve the Accuracy of Continuous Blood Glucose Monitoring

Jia, Z., Huang, L., Liu, H., Huang, Y., Li, W., Pi, X., & Zheng, X. (2019) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-019-03142-7 · Cited by 11 · see citing works

A New Stochastic Approach for Modeling Glycemic Disturbances in Type 2 Diabetes

Clausen, H. G., Knudsen, T., Ahdab, M. A., Aradóttir, T. B., Schmidt, S., Nørgaard, K., & Leth, J. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3074868 · Cited by 10 · see citing works

Calibrating Glucose Sensors at the Edge: A Stress Generation Model for Tiny ML Drift Compensation

Sabatini, A., Cenerini, C., Vollero, L., & Pau, D. P. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4020083 · Cited by 9 · see citing works

Adaptive Unscented Kalman Filter using Maximum Likelihood Estimation

Mahmoudi, Z., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.356 · Cited by 9 · see citing works

Modelling intestinal glucose absorption in premature infants using continuous glucose monitoring data

Knopp, J. L., Signal, M., Harris, D. L., Marics, G., Weston, P. J., Harding, J. E., Tóth‐Heyn, P., Homlok, J., Benyó, B. I., & Chase, J. G. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.10.005 · Cited by 9 · see citing works

The Minimally-Invasive Oral Glucose Minimal Model: Estimation of Gastric Retention, Glucose Rate of Appearance, and Insulin Sensitivity From Type 1 Diabetes Data Collected in Real-Life Conditions

Faggionato, E., Schiavon, M., Ekhlaspour, L., Buckingham, B. A., & Man, C. D. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3324206 · Cited by 8 · see citing works

Identification of CGM Time Delays and Implications for BG Control in T1DM

Reiterer, F., Polterauer, P., Freckmann, G., & del Re, L. (2016) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-32703-7_39 · Cited by 8 · see citing works

Model-based glycaemic control: methodology and initial results from neonatal intensive care

Dickson, J. L., Chase, J. G., Lynn, A. M., & Shaw, G. M. (2016) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2016-0051 · Cited by 8 · see citing works

The state of variability: A vision for descriptors of glycaemia

Zhou, T., Knopp, J. L., & Chase, J. G. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.06.004 · Cited by 8 · see citing works

Comparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric

DeJournett, J., & DeJournett, L. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817711297 · Cited by 7 · see citing works

Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation

Gomez, L., Toye, A. A., Hum, R. S., & Kleinberg, S. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231181138 · Cited by 5 · see citing works

Discrete LPV Modeling of Diabetes Mellitus for Control Purposes

Eigner, G., Siket, M., Szakál, A., Rudas, I. J., & Kovács, L. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00438 · Cited by 5 · see citing works

Calibration of an optical sensor for in vivo blood glucose maeasurenaent

Bagal, T., & Bhole, K. (2019) · International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) · doi:10.1109/icicict46008.2019.8993348 · Cited by 5 · see citing works

Autoencoder-Based Detection of Insulin Pump Faults in Type 1 Diabetes Treatment

Idi, E., Prendin, F., Sparacino, G., & Favero, S. D. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3518233 · Cited by 5 · see citing works

Enhancing Glucose Sensor Models: Modeling the Drop-Outs

Emami, A., RABASA-LHORET, R. P. R., & Haidar, A. (2015) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2014.0309 · Cited by 5 · see citing works

Model predictive control to account for prolonged changes in insulin requirements following exercise in type 1 diabetes

Deichmann, J., & Kaltenbach, H. (2023) · Journal of Process Control · doi:10.1016/j.jprocont.2023.103042 · Cited by 5 · see citing works

Regularized Denoising Method for Retrospective Detection of Pressure Induced Artifacts in Continuous Glucose Monitoring Sensors Data

Manzoni, E., Idi, E., Camerlingo, N., Facchinetti, A., Sparacino, G., & Favero, S. D. (2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2025.3536805 · Cited by 4 · see citing works

The Impact of Interfering Substances on Continuous Glucose Monitors: Part 4: Continuous Glucose Monitoring Interferents: In Vivo Performance, Clinical Implications and Future Standardization

Setford, S. J. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251377026 · Cited by 4 · see citing works

Modeling strategies for CGM data: A scoping review of mechanistic, machine learning, and hybrid approaches in diabetes management

Fera, N., Procopio, A., Zaffino, P., Cutruzzolà, A., Irace, C., & Cosentino, C. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111601 · Cited by 4 · see citing works

Configuration Testing of an Artificial Pancreas System Using a Digital Twin: An Evaluative Case Study

Somers, R. J., Walkinshaw, N., Hierons, R. M., Elliott, D., Iqbal, A., & Walkinshaw, E. (2025) · Software Testing Verification and Reliability · doi:10.1002/stvr.70000 · Cited by 8 · see citing works

The development of advanced mathematical models for continuous glucose sensors

Gao, M. Z., & Orazem, M. E. (2021) · Electrochimica Acta · doi:10.1016/j.electacta.2021.138226 · Cited by 4 · see citing works

Discrete LPV Based Parameter Estimation For TIDM Patients By Using Dual Extended Kalman Filtering Method

Kovács, L., Siket, M., Rudas, I. J., Szakál, A., & Eigner, G. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914014 · Cited by 4 · see citing works

Kalman Filtering of Discrete LPV Diabetes Mellitus Model for Control Purposes

Eigner, G., Siket, M., & Kovács, L. (2018) · IEEE International Symposium on Computational Intelligence and Informatics (CINTI) · doi:10.1109/cinti.2018.8928205 · Cited by 4 · see citing works

Noise spectral analysis and error estimation of continuous glucose monitors under real-life conditions of diabetes patients

Garnica, Ó., Lanchares, J., Velasco, J. M., Hidalgo, J. I., & Botella, M. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101934 · Cited by 4 · see citing works

State Space Temporal Gaussian Processes for Glucose Measurements

Ahdab, M. A., Knudsen, T., & Leth, J. (2022) · European Control Conference (ECC) · doi:10.23919/ecc55457.2022.9838040 · Cited by 4 · see citing works

Noise Reduction in Continuous Blood Glucose Sensor using Physiology based Kalman Filter for Artificial Pancreatic System

Bidarahalli, N., Rajesh, M., Velappan, V., & Krishnan, P. T. (2018) · International Conference on Circuits, Control, Communication and Computing (I4C) · doi:10.1109/cimca.2018.8739339 · Cited by 3 · see citing works

Continuous Glucose Monitoring: Using CGM to Guide Insulin Therapy Virtual Trials Results

Mombaerts, L., Thomas, F., Signal, M., Desaive, T., & Chase, J. G. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.10.124 · Cited by 3 · see citing works

A Modular Open-Source Framework for In-Browser Diabetes Simulation

Peuscher, H., Schrills, T., Eichenlaub, M., & Jørgensen, J. B. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.11.055 · Cited by 3 · see citing works

Efficient Computation of the Continuous-Discrete Extended Kalman Filter Sensitivities Applied to Maximum Likelihood Estimation

Boiroux, D., Ritschel, T. K. S., Poulsen, N. K., Madsen, H., & Jørgensen, J. B. (2019) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc40024.2019.9029672 · Cited by 3 · see citing works

Modeling the error of factory-calibrated continuous glucose monitoring sensors: application to Dexcom G6 sensor data

Vettoretti, M., Favero, S. D., Sparacino, G., & Facchinetti, A. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856790 · Cited by 14 · see citing works

Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices

Facchinetti, A., Del Favero, S., Sparacino, G., & Cobelli, C. (2015) · Medical &amp; Biological Engineering &amp; Computing · doi:10.1007/s11517-014-1226-y · Cited by 80 · see citing works

Model of Sensor Error from Multiple\nSimultaneous Continuous Glucose Monitors\nin the Same Subject

Facchinetti, A., Favero, S. D., Castle, J. R., Ward, W. K., SPARACINO, G., & COBELLI, C. (2012) · Padua Research Archive (University of Padova)

A Simple Method to Model a Continuous Glucose Monitoring Signal

Thomas, F., Pretty, C. G., Dickson, J. L., Signal, M., Shaw, G. M., & Chase, J. G. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.1736 · Cited by 3 · see citing works

Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime

Vettoretti, M., Battocchio, C., Sparacino, G., & Facchinetti, A. (2019) · Sensors · doi:10.3390/s19235320 · Cited by 31 · see citing works