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Data-driven Modeling for Diabetes pp 91–116Cite as

Adaptive Algorithms for Personalized Diabetes Treatment

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Part of the book series: Lecture Notes in Bioengineering ((LNBE))

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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Author information

Authors and Affiliations

  1. Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland

    Elena Daskalaki & Stavroula Mougiakakou

  2. Department of Endocrinology, Diabetes and Clinical Nutrition, Bern University Hospital “Inselspital”, Bern, Switzerland

    Peter Diem

Authors
  1. Elena Daskalaki
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  2. Peter Diem
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  3. Stavroula Mougiakakou
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Corresponding author

Correspondence to Stavroula Mougiakakou .

Editor information

Editors and Affiliations

  1. Biomedical Engineering, University of Southern California, Los Angeles, California, USA

    Vasilis Marmarelis

  2. Electrical and Computer Engineering, Greece, University of Cyprus,Nicosia,Greese and McGill University, Montreal QC, Canada

    Georgios Mitsis

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Daskalaki, E., Diem, P., Mougiakakou, S. (2014). Adaptive Algorithms for Personalized Diabetes Treatment. In: Marmarelis, V., Mitsis, G. (eds) Data-driven Modeling for Diabetes. Lecture Notes in Bioengineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54464-4_4

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

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54463-7

  • Online ISBN: 978-3-642-54464-4

  • eBook Packages: EngineeringEngineering (R0)

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