A model-based approach to synthesizing insulin infusion pump usage parameters for diabetic patients

We present a model-based approach to synthesizing insulin infusion pump usage parameters against varying meal scenarios and physiological conditions. Insulin infusion pumps are commonly used by type-1 diabetic patients to control their blood glucose levels. The amounts of insulin to be infused are calculated based on parameters such as insulin-to-carbohydrate ratios and correction factors that need to be calibrated carefully for each patient. Frequent and careful calibration of these parameters is essential for avoiding complications such as hypoglycemia and hyperglycemia. In this paper, we propose to synthesize optimal parameters for meal bolus calculation starting from models of the patient's insulin-glucose regulatory system and the infusion pump. Various off-the-shelf global optimization techniques are used to search for parameter values that minimize a penalty function defined over the predicted glucose sensor readings. The penalty function “rewards” glucose levels that lie within the prescribed ranges and “penalizes” the occurrence of hypoglycemia and hyperglycemia. We evaluate our approach using a model of the insulin-glucose regulatory system proposed by Dalla Man et al. Using this model, we compare various strategies for optimizing pump usage parameters for a virtual population of in-silico patients.

[1]  Claudio Cobelli,et al.  A System Model of Oral Glucose Absorption: Validation on Gold Standard Data , 2006, IEEE Transactions on Biomedical Engineering.

[2]  Ron Koymans,et al.  Specifying real-time properties with metric temporal logic , 1990, Real-Time Systems.

[3]  Roman Hovorka,et al.  Simulation Environment to Evaluate Closed-Loop Insulin Delivery Systems in Type 1 Diabetes , 2010, Journal of diabetes science and technology.

[4]  Armando Solar-Lezama,et al.  Programming by sketching for bit-streaming programs , 2005, PLDI '05.

[5]  Darrell M. Wilson,et al.  A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator , 2009, Journal of diabetes science and technology.

[6]  Giovanni Sparacino,et al.  Diabetes: Models, Signals, and Control , 2009 .

[7]  Calin Belta,et al.  Parameter Synthesis for Piecewise Affine Systems from Temporal Logic Specifications , 2008, HSCC.

[8]  Sriram Sankaranarayanan,et al.  Monte-carlo techniques for falsification of temporal properties of non-linear hybrid systems , 2010, HSCC '10.

[9]  Sriram Sankaranarayanan,et al.  S-TaLiRo: A Tool for Temporal Logic Falsification for Hybrid Systems , 2011, TACAS.

[10]  George J. Pappas,et al.  Robustness of temporal logic specifications for continuous-time signals , 2009, Theor. Comput. Sci..

[11]  J. Hammersley.  SIMULATION AND THE MONTE CARLO METHOD , 1982 .

[12]  Sriram Sankaranarayanan,et al.  Falsification of temporal properties of hybrid systems using the cross-entropy method , 2012, HSCC '12.

[13]  Claudio Cobelli,et al.  GIM, Simulation Software of Meal Glucose—Insulin Model , 2007, Journal of diabetes science and technology.

[14]  Gary Scheiner,et al.  Think Like a Pancreas: A Practical Guide to Managing Diabetes with Insulin , 2004 .

[15]  M. Morari,et al.  Closed-Loop Control of Blood Glucose , 2007 .

[16]  Lucie M. Gattepaille,et al.  Robustness Analysis and Behavior Discrimination in Enzymatic Reaction Networks , 2011, PloS one.

[17]  R. Hovorka,et al.  Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. , 2004, Physiological measurement.

[18]  R. Bergman.  Minimal Model: Perspective from 2005 , 2006, Hormone Research in Paediatrics.

[19]  Susmit Jha,et al.  Synthesis of insulin pump controllers from safety specifications using Bayesian model validation , 2012, Int. J. Bioinform. Res. Appl..

[20]  George J. Pappas,et al.  Robustness of Temporal Logic Specifications , 2006, FATES/RV.

[21]  Swarat Chaudhuri,et al.  Smoothing a Program Soundly and Robustly , 2011, CAV.

[22]  Claudio Cobelli,et al.  Meal Simulation Model of the Glucose-Insulin System , 2007, IEEE Transactions on Biomedical Engineering.

[23]  François Fages,et al.  Continuous valuations of temporal logic specifications with applications to parameter optimization and robustness measures , 2011, Theor. Comput. Sci..

[24]  R. Hovorka,et al.  Partitioning glucose distribution/transport, disposal, and endogenous production during IVGTT. , 2002, American journal of physiology. Endocrinology and metabolism.

[25]  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.

[26]  Sriram Sankaranarayanan,et al.  Simulating Insulin Infusion Pump Risks by In-Silico Modeling of the Insulin-Glucose Regulatory System , 2012, CMSB.