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
Full text not available from this repository.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 - AI in Health and Nutrition 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, 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: |
05 Dec 2022 14:34 |
URI: |
https://boris.unibe.ch/id/eprint/52908 |
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