Adaptive Algorithms for Personalized Diabetes Treatment

Daskalaki, Eleni; Diem, Peter; Mougiakakou, Stavroula (2014). Adaptive Algorithms for Personalized Diabetes Treatment. In: Marmarelis, Vasilis; Mitsis, Georgios (eds.) Data-driven Modeling for Diabetes: Diagnosis and Treatment. Lecture Notes in Bioengineering (pp. 91-116). Berlin: Springer

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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.

Item Type:

Book Section (Book Chapter)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Diabetes Technology
04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Endocrinology, Diabetology and Clinical Nutrition

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Daskalaki, Eleni; Diem, Peter and Mougiakakou, Stavroula

Subjects:

600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISSN:

2195-271X

ISBN:

978-3-642-54464-4

Series:

Lecture Notes in Bioengineering

Publisher:

Springer

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

18 Sep 2014 11:22

Last Modified:

25 Feb 2016 11:54

URI:

https://boris.unibe.ch/id/eprint/52908

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