A systematic stochastic design strategy achieving an optimal tradeoff between peak BGL and probability of hypoglycaemic events for individuals having type 1 diabetes mellitus
暂无分享,去创建一个
Graham C. Goodwin | Adrian M. Medioli | Bruce R. King | María M. Seron | Tenele Smith | Carmel Smart | G. Goodwin | M. Seron | B. King | C. Smart | Tenele A Smith
[1] J. Geoffrey Chase,et al. A 3D insulin sensitivity prediction model enables more patient-specific prediction and model-based glycaemic control , 2018, Biomed. Signal Process. Control..
[2] C. Cobelli,et al. The UVA/PADOVA Type 1 Diabetes Simulator , 2014, Journal of diabetes science and technology.
[3] Richard D. Braatz,et al. Stochastic model predictive control with joint chance constraints , 2015, Int. J. Control.
[4] Graham C. Goodwin,et al. Control System Design , 2000 .
[5] R. Hovorka,et al. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. , 2004, Physiological measurement.
[6] Christopher E. Hann,et al. A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients , 2011, Comput. Methods Programs Biomed..
[7] Christopher E. Hann,et al. Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care , 2008, Comput. Methods Programs Biomed..
[8] Giovanni Sparacino,et al. Improving Accuracy and Precision of Glucose Sensor Profiles: Retrospective Fitting by Constrained Deconvolution , 2014, IEEE Transactions on Biomedical Engineering.
[9] Henrik Ohlsson,et al. On the estimation of transfer functions, regularizations and Gaussian processes - Revisited , 2012, Autom..
[10] L. Magni,et al. Day-and-Night Closed-Loop Glucose Control in Patients With Type 1 Diabetes Under Free-Living Conditions: Results of a Single-Arm 1-Month Experience Compared With a Previously Reported Feasibility Study of Evening and Night at Home , 2016, Diabetes Care.
[11] J. Geoffrey Chase,et al. STAR Development and Protocol Comparison , 2012, IEEE Transactions on Biomedical Engineering.
[12] David C. Klonoff,et al. Diabetes Technology Update: Use of Insulin Pumps and Continuous Glucose Monitoring in the Hospital , 2018, Diabetes Care.
[13] Graham C. Goodwin,et al. Non-stationary stochastic embedding for transfer function estimation , 1999, Autom..
[14] Pu Li,et al. Advances and applications of chance-constrained approaches to systems optimisation under uncertainty , 2013, Int. J. Syst. Sci..
[15] Patricio Colmegna,et al. Analysis of three T1DM simulation models for evaluating robust closed-loop controllers , 2014, Comput. Methods Programs Biomed..
[16] Giuseppe De Nicolao,et al. MPC based Artificial Pancreas: Strategies for individualization and meal compensation , 2012, Annu. Rev. Control..
[17] J. Geoffrey Chase,et al. Stochastic Targeted (STAR) Glycemic Control: Design, Safety, and Performance , 2012, Journal of diabetes science and technology.
[18] Garry M. Steil,et al. Identification of Intraday Metabolic Profiles during Closed-Loop Glucose Control in Individuals with Type 1 Diabetes , 2009, Journal of diabetes science and technology.
[19] Josep Vehí,et al. A review of personalized blood glucose prediction strategies for T1DM patients , 2017, International journal for numerical methods in biomedical engineering.
[20] Patricio Colmegna,et al. Linear parameter-varying model to design control laws for an artificial pancreas , 2018, Biomed. Signal Process. Control..
[21] Levente Kovács,et al. Induced L2-norm minimization of glucose-insulin system for Type I diabetic patients , 2011, Comput. Methods Programs Biomed..
[22] Claudio Cobelli,et al. Toward a Run-to-Run Adaptive Artificial Pancreas: In Silico Results , 2018, IEEE Transactions on Biomedical Engineering.
[23] Malgorzata E. Wilinska,et al. Modeling Day-to-Day Variability of Glucose–Insulin Regulation Over 12-Week Home Use of Closed-Loop Insulin Delivery , 2017, IEEE Transactions on Biomedical Engineering.
[24] Alexander Shapiro,et al. Lectures on Stochastic Programming: Modeling and Theory , 2009 .
[25] Ahmad Haidar,et al. Stochastic Virtual Population of Subjects With Type 1 Diabetes for the Assessment of Closed-Loop Glucose Controllers , 2013, IEEE Transactions on Biomedical Engineering.
[26] Tianshi Chen,et al. A shift in paradigm for system identification , 2019, Int. J. Control.
[27] Jeroen van den Hoven,et al. Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm , 2018, Front. Genet..
[28] Lalo Magni,et al. Glucose-insulin model identified in free-living conditions for hypoglycaemia prevention , 2018 .
[29] P. McElduff,et al. Optimizing the combination insulin bolus split for a high‐fat, high‐protein meal in children and adolescents using insulin pump therapy , 2017, Diabetic medicine : a journal of the British Diabetic Association.
[30] R. Hovorka,et al. Simulation models for in silico testing of closed-loop glucose controllers in type 1 diabetes , 2008 .
[31] Guido Freckmann,et al. Performance and Usability of Three Systems for Continuous Glucose Monitoring in Direct Comparison , 2019, Journal of diabetes science and technology.
[32] Eyal Dassau,et al. Periodic zone-MPC with asymmetric costs for outpatient-ready safety of an artificial pancreas to treat type 1 diabetes , 2016, Autom..
[33] Christopher G. Pretty,et al. Estimation of the insulin sensitivity profile for the stochastic variant of the ICING model , 2016, 2016 IEEE 20th Jubilee International Conference on Intelligent Engineering Systems (INES).
[34] Eyal Dassau,et al. Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance , 2018, Autom..
[35] 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.
[36] Christopher G. Pretty,et al. Analysis of Stochastic Noise of Blood-Glucose Dynamics , 2017 .
[37] Claude H. Moog,et al. A Long-Term Model of the Glucose–Insulin Dynamics of Type 1 Diabetes , 2015, IEEE Transactions on Biomedical Engineering.
[38] Patrick McElduff,et al. Extended insulin boluses cannot control postprandial glycemia as well as a standard bolus in children and adults using insulin pump therapy , 2014, BMJ Open Diabetes Research and Care.
[39] Tor Arne Johansen,et al. On Tikhonov regularization, bias and variance in nonlinear system identification , 1997, Autom..
[40] Lennart Ljung,et al. System Identification: Theory for the User , 1987 .
[41] Yeong Shiong Chiew,et al. 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 , 2018, BioMedical Engineering OnLine.
[42] Graham C. Goodwin,et al. Dynamic System Identification: Experiment Design and Data Analysis , 2012 .
[43] C. Cobelli,et al. In Silico Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes , 2009, Journal of diabetes science and technology.
[44] Lennart Ljung,et al. Model Error Modeling and Stochastic Embedding , 2015 .
[45] Richard D. Braatz,et al. Stochastic model predictive control with joint chance constraints , 2015, Int. J. Control.
[46] Eyal Dassau,et al. Switched LPV Glucose Control in Type 1 Diabetes , 2016, IEEE Transactions on Biomedical Engineering.
[47] Boris Kovatchev,et al. Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors , 2008, Journal of diabetes science and technology.
[48] Levente Kovcs,et al. Linear parameter varying (LPV) based robust control of type-I diabetes driven for real patient data , 2017 .
[49] Claudio Cobelli,et al. Artificial Pancreas: Model Predictive Control Design from Clinical Experience , 2013, Journal of diabetes science and technology.
[50] Claudio Cobelli,et al. One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator , 2016, IEEE Transactions on Biomedical Engineering.
[51] Graham C. Goodwin,et al. Estimated Transfer Functions with Application to Model Order Selection , 1992 .
[52] Claudio Cobelli,et al. The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day , 2018, Journal of diabetes science and technology.