Advanced carbohydrate counting: An engineering perspective
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[1] Christofer Toumazou,et al. Method for automatic adjustment of an insulin bolus calculator: In silico robustness evaluation under intra-day variability , 2015, Comput. Methods Programs Biomed..
[2] E. Renard,et al. Development of a Smartphone Application to Capture Carbohydrate, Lipid, and Protein Contents of Daily Food , 2015, Journal of diabetes science and technology.
[3] J. Mahoney,et al. Importance of Blood Glucose Meter and Carbohydrate Estimation Accuracy , 2012, Journal of diabetes science and technology.
[4] G. Slama,et al. Correlation Between the Nature and Amount of Carbohydrate in Meal Intake and Insulin Delivery by the Artificial Pancreas in 24 Insulin-dependent Diabetics , 1981, Diabetes.
[5] Niels Kjølstad Poulsen,et al. An Adaptive Nonlinear Basal-Bolus Calculator for Patients With Type 1 Diabetes , 2017, Journal of diabetes science and technology.
[6] K. Kulkarni,et al. Carbohydrate Counting: A Practical Meal-Planning Option for People With Diabetes , 2005 .
[7] H. Lunt,et al. Self‐reported Changes in Capillary Glucose and Insulin Requirements During the Menstrual Cycle , 1996, Diabetic medicine : a journal of the British Diabetic Association.
[8] G. Colditz,et al. Life Years Lost and Lifetime Health Care Expenditures Associated With Diabetes in the U.S., National Health Interview Survey, 1997–2000 , 2014, Diabetes Care.
[9] Dong Shen,et al. Optimization of insulin pump therapy based on high order run-to-run control scheme , 2015, Comput. Methods Programs Biomed..
[10] K. Nørgaard,et al. Effects of advanced carbohydrate counting in patients with Type 1 diabetes: a systematic review , 2014, Diabetic medicine : a journal of the British Diabetic Association.
[11] Exponential increase in postprandial blood-glucose exposure with increasing carbohydrate loads using a linear carbohydrate-to-insulin ratio. , 2013, South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde.
[12] Claudio Cobelli,et al. Diurnal Pattern of Insulin Action in Type 1 Diabetes , 2013, Diabetes.
[13] E. Campos-Náñez,et al. Clinical Impact of Blood Glucose Monitoring Accuracy: An In-Silico Study , 2017, Journal of diabetes science and technology.
[14] Luigi del Re,et al. Identification of diurnal patterns in insulin action from measured CGM data for patients with T1DM , 2015, 2015 European Control Conference (ECC).
[15] S. Genuth,et al. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. , 1993, The New England journal of medicine.
[16] S. Edelman,et al. Differences in Use of Glucose Rate of Change (ROC) Arrows to Adjust Insulin Therapy Among Individuals With Type 1 and Type 2 Diabetes Who Use Continuous Glucose Monitoring (CGM) , 2016, Journal of diabetes science and technology.
[17] M. Fowler. Microvascular and Macrovascular Complications of Diabetes , 2008, Clinical Diabetes.
[18] P. Halfon,et al. Correlation Between Amount of Carbohydrate in Mixed Meals and Insulin Delivery by Artificial Pancreas in seven IDDM Subjects , 1989, Diabetes Care.
[19] Christofer Toumazou,et al. Case-Based Reasoning for Insulin Bolus Advice , 2017, Journal of diabetes science and technology.
[20] R Rabasa-Lhoret,et al. Effects of meal carbohydrate content on insulin requirements in type 1 diabetic patients treated intensively with the basal-bolus (ultralente-regular) insulin regimen. , 1999, Diabetes care.
[21] R. Beck,et al. Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials , 2018, Diabetes Care.
[22] Ping Zhang,et al. The Lifetime Cost of Diabetes and Its Implications for Diabetes Prevention , 2014, Diabetes Care.
[23] D. Schoenfeld,et al. Translating the A1C Assay Into Estimated Average Glucose Values , 2008, Diabetes Care.
[24] S. Seino,et al. Diurnal variation of carbohydrate insulin ratio in adult type 1 diabetic patients treated with continuous subcutaneous insulin infusion , 2013, Journal of diabetes investigation.
[25] Christofer Toumazou,et al. Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra-day variability , 2017, Comput. Methods Programs Biomed..
[26] Luigi del Re,et al. Deviation analysis of clinical studies as tool to tune and assess performance of diabetes control algorithms , 2016, 2016 IEEE Conference on Control Applications (CCA).
[27] Howard C. Zisser,et al. Clinical Update on Optimal Prandial Insulin Dosing Using a Refined Run-to-Run Control Algorithm , 2009, Journal of diabetes science and technology.
[28] J. Reifman,et al. Bolus Estimation—Rethinking the Effect of Meal Fat Content , 2015, Diabetes technology & therapeutics.
[29] Giovanni Sparacino,et al. Online Calibration of Glucose Sensors From the Measured Current by a Time-Varying Calibration Function and Bayesian Priors , 2016, IEEE Transactions on Biomedical Engineering.
[30] Behjat Siddiquie,et al. “Snap-n-Eat” , 2015, Journal of diabetes science and technology.
[31] C Cobelli,et al. Insulin sensitivity from meal tolerance tests in normal subjects: a minimal model index. , 2000, The Journal of clinical endocrinology and metabolism.
[32] C. Cobelli,et al. The UVA/PADOVA Type 1 Diabetes Simulator , 2014, Journal of diabetes science and technology.
[33] Denis Gillet,et al. A therapy parameter-based model for predicting blood glucose concentrations in patients with type 1 diabetes , 2015, Comput. Methods Programs Biomed..
[34] Claude H. Moog,et al. A Long-Term Model of the Glucose–Insulin Dynamics of Type 1 Diabetes , 2015, IEEE Transactions on Biomedical Engineering.
[35] Josep Vehí,et al. Calculation of the Best Basal–Bolus Combination for Postprandial Glucose Control in Insulin Pump Therapy , 2011, IEEE Transactions on Biomedical Engineering.
[36] Giovanni Sparacino,et al. In Silico Assessment of Literature Insulin Bolus Calculation Methods Accounting for Glucose Rate of Change , 2018, Journal of diabetes science and technology.
[37] L. Re,et al. Identification of Mixed-Meal Effects on Insulin Needs and Glycemic Control , 2018 .
[38] A. King,et al. A Prospective Evaluation of Insulin Dosing Recommendations in Patients with Type 1 Diabetes at near Normal Glucose Control: Basal Dosing , 2007, Journal of diabetes science and technology.
[39] J. Edge,et al. Children and adolescents on intensive insulin therapy maintain postprandial glycaemic control without precise carbohydrate counting , 2009, Diabetic medicine : a journal of the British Diabetic Association.
[40] Comparative Dose Accuracy of Durable and Patch Insulin Infusion Pumps , 2013, Journal of diabetes science and technology.
[41] John R. Smith,et al. Snap, Eat, RepEat: A Food Recognition Engine for Dietary Logging , 2016, MADiMa @ ACM Multimedia.
[42] J. Edge,et al. Can children with Type 1 diabetes and their caregivers estimate the carbohydrate content of meals and snacks? , 2009, Diabetic medicine : a journal of the British Diabetic Association.
[43] A. Morris,et al. Frequency of severe hypoglycemia requiring emergency treatment in type 1 and type 2 diabetes: a population-based study of health service resource use. , 2003, Diabetes care.
[44] Pierre Combris,et al. OQALI: A French database on processed foods ☆ , 2011 .
[45] K. Barnard,et al. Use of an Insulin Bolus Advisor Improves Glycemic Control in Multiple Daily Insulin Injection (MDI) Therapy Patients With Suboptimal Glycemic Control , 2013, Diabetes Care.
[46] Stephen D Patek,et al. Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring. , 2015, Diabetes technology & therapeutics.
[47] R. Beck,et al. Factors Associated With Diabetes-Specific Health-Related Quality of Life in Youth With Type 1 Diabetes: The Global TEENs Study , 2017, Diabetes Care.
[48] A J Scheen,et al. Roles of circadian rhythmicity and sleep in human glucose regulation. , 1997, Endocrine reviews.
[49] Graham C. Goodwin,et al. A fundamental control limitation for linear positive systems with application to Type 1 diabetes treatment , 2015, Autom..
[50] Josep Vehí,et al. Open-loop glucose control: Automatic IOB-based super-bolus feature for commercial insulin pumps , 2018, Comput. Methods Programs Biomed..
[51] Luigi del Re,et al. A model based bolus calculator for blood glucose control in type 1 diabetes , 2014, 2014 American Control Conference.
[52] P. Ladyzynski,et al. Accuracy of Automatic Carbohydrate, Protein, Fat and Calorie Counting Based on Voice Descriptions of Meals in People with Type 1 Diabetes , 2018, Nutrients.
[53] L. Re,et al. Impact of Carbohydrate Counting Errors on Glycemic Control in Type 1 Diabetes , 2018 .
[54] Ewa Pańkowska,et al. Bolus Calculator with Nutrition Database Software, a New Concept of Prandial Insulin Programming for Pump Users , 2010, Journal of diabetes science and technology.
[55] Guido Freckmann,et al. Significance and Reliability of MARD for the Accuracy of CGM Systems , 2017, Journal of diabetes science and technology.
[56] G. Charpentier,et al. Accuracy of a New Patch Pump Based on a Microelectromechanical System (MEMS) Compared to Other Commercially Available Insulin Pumps , 2014, Journal of diabetes science and technology.
[57] Giovanni Sparacino,et al. A Model of Self-Monitoring Blood Glucose Measurement Error , 2017, Journal of diabetes science and technology.
[58] E. Campos-Náñez,et al. Effect of BGM Accuracy on the Clinical Performance of CGM: An In-Silico Study , 2017, Journal of diabetes science and technology.
[59] F. Doyle,et al. Detection of a Meal Using Continuous Glucose Monitoring , 2008, Diabetes Care.
[60] Luigi del Re,et al. Performance assessment of estimation methods for CIR/ISF in bolus calculators , 2015 .
[61] R. Bergenstal,et al. Adjust to Target in Type 2 Diabetes , 2008, Diabetes Care.
[62] Giovanni Sparacino,et al. Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices , 2014, Medical & Biological Engineering & Computing.
[63] Howard C. Zisser,et al. Prandial Insulin Dosing Using Run-to-Run Control , 2007, Diabetes Care.
[64] Claudio Cobelli,et al. Quantitative Estimation of Insulin Sensitivity in Type 1 Diabetic Subjects Wearing a Sensor-Augmented Insulin Pump , 2014, Diabetes Care.
[65] H. Marston,et al. Food Composition Tables. , 1944 .
[66] Ali Cinar,et al. Meal Detection in Patients With Type 1 Diabetes: A New Module for the Multivariable Adaptive Artificial Pancreas Control System , 2016, IEEE Journal of Biomedical and Health Informatics.
[67] S. Colagiuri,et al. Efficacy of carbohydrate counting in type 1 diabetes: a systematic review and meta-analysis. , 2014, The lancet. Diabetes & endocrinology.
[68] Marios Anthimopoulos,et al. Computer Vision-Based Carbohydrate Estimation for Type 1 Patients With Diabetes Using Smartphones , 2015, Journal of diabetes science and technology.
[69] Hyunjin Lee,et al. A closed-loop artificial pancreas based on model predictive control: Human-friendly identification and automatic meal disturbance rejection , 2009, Biomed. Signal Process. Control..
[70] F. Doyle,et al. Design of the Glucose Rate Increase Detector , 2014, Journal of diabetes science and technology.
[71] S. Edelman,et al. Use of Glucose Rate of Change Arrows to Adjust Insulin Therapy Among Individuals with Type 1 Diabetes Who Use Continuous Glucose Monitoring. , 2016, Diabetes technology & therapeutics.
[72] Giovanni Sparacino,et al. A Neural-Network-Based Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose Monitoring , 2018, Journal of diabetes science and technology.
[73] S. Saydah,et al. The Prevalence of Meeting A1C, Blood Pressure, and LDL Goals Among People With Diabetes, 1988–2010 , 2013, Diabetes Care.
[74] Rolf Johansson,et al. Direct continuous time system identification of MISO transfer function models applied to type 1 diabetes , 2011, IEEE Conference on Decision and Control and European Control Conference.
[75] Sergio Guadarrama,et al. Im2Calories: Towards an Automated Mobile Vision Food Diary , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[76] P. McElduff,et al. Influence of dietary protein on postprandial blood glucose levels in individuals with Type 1 diabetes mellitus using intensive insulin therapy , 2015, Diabetic medicine : a journal of the British Diabetic Association.
[77] Robert S Parker,et al. Dynamic modeling of free fatty acid, glucose, and insulin: an extended "minimal model". , 2006, Diabetes technology & therapeutics.
[78] L. Heinemann,et al. Insulin Injection Into Lipohypertrophic Tissue: Blunted and More Variable Insulin Absorption and Action and Impaired Postprandial Glucose Control , 2016, Diabetes Care.
[79] Alan Bernjak,et al. Diurnal Differences in Risk of Cardiac Arrhythmias During Spontaneous Hypoglycemia in Young People With Type 1 Diabetes , 2017, Diabetes Care.
[80] LazaroCaterina,et al. Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System , 2018 .
[81] Marlena Błazik,et al. Does the fat-protein meal increase postprandial glucose level in type 1 diabetes patients on insulin pump: the conclusion of a randomized study. , 2012, Diabetes technology & therapeutics.
[82] Francis J. Doyle,et al. Run-to-run control of blood glucose concentrations for people with type 1 diabetes mellitus , 2006, IEEE Transactions on Biomedical Engineering.
[84] Josep Vehí,et al. Unannounced Meals in the Artificial Pancreas: Detection Using Continuous Glucose Monitoring , 2018, Sensors.
[85] John Walsh,et al. Guidelines for Insulin Dosing in Continuous Subcutaneous Insulin Infusion Using New Formulas from a Retrospective Study of Individuals with Optimal Glucose Levels , 2010, Journal of diabetes science and technology.
[86] Jenine Y Stone,et al. Review of a commercially available hybrid closed-loop insulin-delivery system in the treatment of Type 1 diabetes. , 2018, Therapeutic delivery.
[87] Luigi del Re,et al. Hybrid in silico evaluation of insulin dosing algorithms in diabetes , 2019 .
[88] G. Steil,et al. Impact of Fat, Protein, and Glycemic Index on Postprandial Glucose Control in Type 1 Diabetes: Implications for Intensive Diabetes Management in the Continuous Glucose Monitoring Era , 2015, Diabetes Care.
[89] Luigi del Re,et al. Continuous-time interval model identification of blood glucose dynamics for type 1 diabetes , 2014, Int. J. Control.
[90] Peter G. Jacobs,et al. Nonadjunctive Use of Continuous Glucose Monitoring for Diabetes Treatment Decisions , 2016, Journal of diabetes science and technology.
[91] P. McElduff,et al. In children using intensive insulin therapy, a 20‐g variation in carbohydrate amount significantly impacts on postprandial glycaemia , 2012, Diabetic medicine : a journal of the British Diabetic Association.
[92] Christofer Toumazou,et al. Advanced Insulin Bolus Advisor Based on Run-To-Run Control and Case-Based Reasoning , 2015, IEEE Journal of Biomedical and Health Informatics.
[93] A. Brazeau,et al. Carbohydrate counting accuracy and blood glucose variability in adults with type 1 diabetes. , 2013, Diabetes research and clinical practice.
[94] P. Davidson,et al. Analysis of guidelines for basal-bolus insulin dosing: basal insulin, correction factor, and carbohydrate-to-insulin ratio. , 2008, Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists.
[95] Elena Toschi,et al. Optimized Mealtime Insulin Dosing for Fat and Protein in Type 1 Diabetes: Application of a Model-Based Approach to Derive Insulin Doses for Open-Loop Diabetes Management , 2016, Diabetes Care.
[96] T. Wolever,et al. Sugars and fat have different effects on postprandial glucose responses in normal and type 1 diabetic subjects. , 2011, Nutrition, metabolism, and cardiovascular diseases : NMCD.
[97] Ali Cinar,et al. Meal Detection and Carbohydrate Estimation Using Continuous Glucose Sensor Data , 2017, IEEE Journal of Biomedical and Health Informatics.
[98] Marc D. Breton,et al. Empirical Representation of Blood Glucose Variability in a Compartmental Model , 2016 .
[99] N. Nagelkerke,et al. Accurate Carbohydrate Counting Is an Important Determinant of Postprandial Glycemia in Children and Adolescents With Type 1 Diabetes on Insulin Pump Therapy , 2017, Journal of diabetes science and technology.
[100] P. Ladyzynski,et al. Efficacy of automatic bolus calculator with automatic speech recognition in patients with type 1 diabetes: A randomized cross‐over trial , 2018, Journal of diabetes.
[101] L. Weiss,et al. Impact of intensive nutritional education with carbohydrate counting on diabetes control in type 2 diabetic patients , 2010, Patient preference and adherence.
[102] Eyal Dassau,et al. Novel Insulin Delivery Profiles for Mixed Meals for Sensor-Augmented Pump and Closed-Loop Artificial Pancreas Therapy for Type 1 Diabetes Mellitus , 2014, Journal of diabetes science and technology.
[103] Neha J. Parikh,et al. Model-Based Sensor-Augmented Pump Therapy , 2013, Journal of diabetes science and technology.
[104] Boris Kovatchev,et al. Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors , 2008, Journal of diabetes science and technology.
[105] Eyal Dassau,et al. Coordinated Basal—Bolus Infusion for Tighter Postprandial Glucose Control in Insulin Pump Therapy , 2009, Journal of diabetes science and technology.
[106] C. Sinding. Making the Unit of Insulin: Standards, Clinical Work, and Industry, 1920-1925 , 2002, Bulletin of the history of medicine.
[107] L. Aronne,et al. Food Order Has a Significant Impact on Postprandial Glucose and Insulin Levels , 2015, Diabetes Care.
[108] John Walsh,et al. Confusion Regarding Duration of Insulin Action , 2014, Journal of diabetes science and technology.
[109] Claudio Cobelli,et al. Insulin Sensitivity Index-Based Optimization of Insulin to Carbohydrate Ratio: In Silico Study Shows Efficacious Protection Against Hypoglycemic Events Caused by Suboptimal Therapy. , 2018, Diabetes technology & therapeutics.
[110] G. Steil,et al. Dietary Fat Acutely Increases Glucose Concentrations and Insulin Requirements in Patients With Type 1 Diabetes , 2013, Diabetes Care.
[111] Luigi del Re,et al. Estimating Interval Process Models for Type 1 Diabetes for Robust Control Design , 2011 .
[112] Niels Kjølstad Poulsen,et al. Sensor-based detection and estimation of meal carbohydrates for people with diabetes , 2019, Biomed. Signal Process. Control..
[113] Steven V. Edelman,et al. Recommendations for Using Real-Time Continuous Glucose Monitoring (rtCGM) Data for Insulin Adjustments in Type 1 Diabetes , 2016, Journal of diabetes science and technology.
[114] Christina Schmid,et al. Evaluation of the Performance of a Novel System for Continuous Glucose Monitoring , 2013, Journal of diabetes science and technology.
[115] Eyal Dassau,et al. Modeling the Effects of Subcutaneous Insulin Administration and Carbohydrate Consumption on Blood Glucose , 2010, Journal of diabetes science and technology.