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Data-Driven and Minimal-Type Compartmental Insulin-Glucose Models: Theory and Applications Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Adaptive Algorithms for Personalized Diabetes Treatment

Authors : Elena Daskalaki, Peter Diem, Stavroula Mougiakakou

Published in: Data-driven Modeling for Diabetes

Publisher: Springer Berlin Heidelberg

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Abstract

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.

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previous chapter Hypoglycemia Prevention Using Low Glucose Suspend Systems
next chapter Pitfalls in Model Identification: Examples from Glucose-Insulin Modelling
Literature
1.
Lof J et al (1998) An adaptive control algorithm for optimization of intensity modulated radiotherapy considering uncertainties in beam profiles, patient set-up and internal organ motion. Phys Med Biol 43:1605–1628CrossRef
2.
Yu C et al (2011) A model-free adaptive control to a blood pump based on heart rate. ASAIO J 57(4):262–267CrossRef
3.
Jiang X et al (2012) A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support. J Am Med Inform Assoc 19:e137–e144CrossRef
4.
Torshabi AE (2013) An adaptive fuzzy prediction model for real time tumor tracking in radiotherapy via external surrogates. J Appl Clin Med Phys 14(1):4008
5.
Wu X et al (2011) Segmentation and reconstruction of vascular structures for 3D real-time simulation. Med Image Anal 15(1):22–34CrossRef
6.
Möller B et al (2011) Adaptive segmentation of particles and cells for fluorescent microscope imaging. Computer vision, imaging and computer graphics. Theory and applications. Springer, Heidelberg, pp 154–167
7.
Liu L et al (2013) Adaptive segmentation of magnetic resonance images with intensity inhomogeneity using level set method. Magn Reson Imaging 31(4):567–574
8.
Hovorka R et al (2004) Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol Meas 25:905–920CrossRef
9.
Magni L et al (2009) Run-to-run tuning of model predictive control for type 1 diabetes subjects. Silico Trial J Diabetes Sci Technol 3(5):1091–1098CrossRef
10.
Zisser H et al (2005) Run-to-run control of meal-related insulin dosing. Diab Technol Ther 7(1):48–57CrossRef
11.
Wand Y et al (2010) Automatic bolus and adaptive basal algorithm for the artificial pancreas b-cell. Diab Technol Ther 12:879–887CrossRef
12.
Miller S et al (2011) Automatic learning algorithm for the MD-Logic artificial pancreas system. Diab Technol Ther 13:983–990CrossRef
13.
Eren-Oruklu M et al (2010) Hypoglycemia prediction with subject-specific recursive time-series models. J Diab Sci Technol 4(1):25–33CrossRef
14.
Meriyan EO et al (2012) Adaptive system identification for estimating future glucose concentrations and hypoglycemia alarms. Automatica 48:1892–1897CrossRefMATH
15.
Daskalaki E, Diem P, Mougiakakou S (2013) An Actor-Critic based controller for glucose regulation in type 1 diabetes. Comput Methods Programs Biomed 109(2):116–125CrossRef
16.
Daskalaki E, Prountzou A, Diem P, Mougiakakou S (2012) Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients. Diab Technol Ther 14(2):168–174CrossRef
17.
Daskalaki E, Nørgaard K, Prountzou A, Züger T, Diem P, Mougiakakou S (2013) An early-warning system for hypoglycemic/hyperglycemic events based on fusion of adaptive prediction models. J Diab Sci Technol 7(3):689–698
18.
Sutton RS, Barto AG (1998) Reinforcement learning. MIT Press, Cambridge
19.
20.
Szepesvari C (2010) Algorithms for reinforcement learning - Synthesis lectures on artificial intelligence and machine learning. Morgan & Claypool Publishers
21.
Tsitsiklis JN, Van Roy B (1997) An analysis of temporal-difference learning with function approximation. IEEE Trans Autom Control 42(5):674–690CrossRefMATH
22.
Hlaváčková-Schindler K, Paluš M, Vejmelka M, Bhattacharya J (2007) Causality detection based on information-theoretic approaches in time series analysis. Phys Rep 441(1):1–46CrossRef
23.
Schreiber T (2000) Measuring information transfer. Phys Rev Lett 85(2):461–464CrossRef
24.
Williams PL, Beer RD (2011) Generalized measures of information transfer, preprint arXiv. 1102.1507
25.
Lee J, Nemati S, Silva I, Edwards BA, Butler JP, Malhotra A (2012) Transfer entropy estimation and directional coupling change detection in biomedical time series. Biomed Eng Online 11:19CrossRef
26.
Butte AJ, Kohane IS (2000) Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput 5:418–429
27.
Patek SD, Bequette BW, Breton M, Buckingham BA, Dassau E, Doyle FJ III, Lum J, Magni L, Zisser H (2009) In silico preclinical trials: methodology and engineering guide to closed-loop control in type 1 diabetes mellitus. J Diab Sci Technol 3:269–282CrossRef
28.
Dalla Man C et al (2007) Meal simulation model of the glucose-insulin system. IEEE Trans Biomed Eng 54(10):1740–1749
29.
Magni L, Raimondo DM, Dalla Man C, Breton M, Patek S, De Nicolao G, Cobelli C, Kovatchev BP (2008) Evaluating the efficacy of closed-loop glucose regulation via control-variability grid analysis. J Diab Sci Technol (Online) 2:630–635
30.
Kovatchev BP, Cox DJ, Gonder-Frederick LA, Young-Hyman D, Schlundt D, Clarke W (1998) Assessment of risk for severe hypoglycemia among adults with IDDM: validation of the low blood glucose index. Diabetes Care 21(11):1870–1875CrossRef
31.
Bosnic Z, Kononenko I (2010) Correction of regression predictions using the secondary learner on the sensitivity analysis outputs. Comput Inform 29:929–946
32.
Zhang C et al (2000) Particle swarm optimization for evolving artificial neural network. IEEE Int Conf Syst Man Cybern 4:2487–2490
33.
Haykin S (1999) Neural Networks: a comprehensive foundation, 2nd edn. Prentice-Hall Inc., New Jersey, p 07458
34.
Werbos PJ (1990) Back propagation through time, what it does and how to do it. Proc IEEE 78:1550–1560CrossRef
35.
Williams R, Zipser D (1995) Gradient based algorithms for recurrent NN and their computational complexity. In: Chauvin Y, Rumelhart DE (eds) Back-propagation: theory, architecture, and applications. Lawrence Erlbaum, Hillsdale
36.
Williams R, Zipser D (1989) A learning algorithm for continually running fully recurrent NN. Neural Comput 1:270–280CrossRef
37.
Tikhonov AN, Arsenin VY (1977) Solutions of Ill-posed problems. Winston, WashingtonMATH
38.
Mougiakakou S, Prountzou A, Iliopoulou D, Nikita K, Vazeou A, Bartsokas C (2006) Neural network based glucose–insulin metabolism models for children with type 1 diabetes. Conf Proc IEEE Eng Med Biol Soc 1:3545–3548CrossRef
Metadata
Title
Adaptive Algorithms for Personalized Diabetes Treatment
Authors
Elena Daskalaki
Peter Diem
Stavroula Mougiakakou
Copyright Year
2014
Publisher
Springer Berlin Heidelberg
Book
Data-driven Modeling for Diabetes

Print ISBN: 978-3-642-54463-7

Electronic ISBN: 978-3-642-54464-4

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-642-54464-4_4