Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  John N. Tsitsiklis,et al.  Analysis of temporal-difference learning with function approximation , 1996, NIPS 1996.

[3]  Yu Li,et al.  Particle swarm optimisation for evolving artificial neural network , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[4]  Ali Cinar,et al.  Adaptive system identification for estimating future glucose concentrations and hypoglycemia alarms , 2012, Autom..

[5]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[6]  K. Hlavácková-Schindler,et al.  Causality detection based on information-theoretic approaches in time series analysis , 2007 .

[7]  S.G. Mougiakakou,et al.  Neural Network based Glucose - Insulin Metabolism Models for Children with Type 1 Diabetes , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  E. Daskalaki,et al.  Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients. , 2012, Diabetes technology & therapeutics.

[9]  B P Kovatchev,et al.  Assessment of risk for severe hypoglycemia among adults with IDDM: validation of the low blood glucose index. , 1998, Diabetes care.

[10]  Eyal Dassau,et al.  In Silico Preclinical Trials: Methodology and Engineering Guide to Closed-Loop Control in Type 1 Diabetes Mellitus , 2009, Journal of diabetes science and technology.

[11]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[12]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[13]  Ali Cinar,et al.  Hypoglycemia Prediction with Subject-Specific Recursive Time-Series Models , 2010, Journal of diabetes science and technology.

[14]  H. Zisser,et al.  Run-to-run control of meal-related insulin dosing. , 2005, Diabetes technology & therapeutics.

[15]  Yves Chauvin,et al.  Back-Propagation: Theory, Architecture, and Applications , 1995 .

[16]  Stephane Cotin,et al.  Segmentation and reconstruction of vascular structures for 3D real-time simulation , 2011, Medical Image Anal..

[17]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[18]  Atul Malhotra,et al.  Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series , 2012, Biomedical engineering online.

[19]  Qi Zhang,et al.  Adaptive segmentation of magnetic resonance images with intensity inhomogeneity using level set method. , 2013, Magnetic resonance imaging.

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

[21]  K. Nørgaard,et al.  An Early Warning System for Hypoglycemic/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models , 2013, Journal of diabetes science and technology.

[22]  Marco Riboldi,et al.  An adaptive fuzzy prediction model for real time tumor tracking in radiotherapy via external surrogates , 2013, Journal of applied clinical medical physics.

[23]  John N. Tsitsiklis,et al.  Simulation-based optimization of Markov reward processes , 2001, IEEE Trans. Autom. Control..

[24]  Stefan Posch,et al.  Adaptive Segmentation of Particles and Cells for Fluorescent Microscope Imaging , 2010, VISIGRAPP.

[25]  E. Atlas,et al.  Automatic learning algorithm for the MD-logic artificial pancreas system. , 2011, Diabetes technology & therapeutics.

[26]  Stavroula G. Mougiakakou,et al.  An Actor-Critic based controller for glucose regulation in type 1 diabetes , 2013, Comput. Methods Programs Biomed..

[27]  A. Brahme,et al.  An adaptive control algorithm for optimization of intensity modulated radiotherapy considering uncertainties in beam profiles, patient set-up and internal organ motion. , 1998, Physics in medicine and biology.

[28]  Jihoon Kim,et al.  A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support , 2012, J. Am. Medical Informatics Assoc..

[29]  I S Kohane,et al.  Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[30]  F. Doyle,et al.  Automatic bolus and adaptive basal algorithm for the artificial pancreatic β-cell. , 2010, Diabetes technology & therapeutics.

[31]  Igor Kononenko,et al.  Correction of Regression Predictions Using the Secondary Learner on the Sensitivity Analysis Outputs , 2010, Comput. Informatics.

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

[33]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[34]  Marco Forgione,et al.  Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: In Silico Trial , 2009, Journal of diabetes science and technology.

[35]  L. Magni,et al.  Evaluating the Efficacy of Closed-Loop Glucose Regulation via Control-Variability Grid Analysis , 2008, Journal of diabetes science and technology.

[36]  B. Gao,et al.  A Model-Free Adaptive Control to a Blood Pump Based on Heart Rate , 2011, ASAIO journal.