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Automatic adaptation of Basal insulin using sensor-augmented pump therapy

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Title: Automatic adaptation of Basal insulin using sensor-augmented pump therapy
Authors: Herrero, P
Bondia, J
Giménez, M
Oliver, N
Georgiou, P
Item Type: Journal Article
Abstract: BACKGROUND: People with insulin-dependent diabetes rely on an intensified insulin regimen. Despite several guidelines, they are usually impractical and fall short in achieving optimal glycemic outcomes. In this work, a novel technique for automatic adaptation of the basal insulin profile of people with diabetes on sensor-augmented pump therapy is presented. METHODS: The presented technique is based on a run-to-run control law that overcomes some of the limitations of previously proposed methods. To prove its validity, an in silico validation was performed. Finally, the artificial intelligence technique of case-based reasoning is proposed as a potential solution to deal with variability in basal insulin requirements. RESULTS: Over a period of 4 months, the proposed run-to-run control law successfully adapts the basal insulin profile of a virtual population (10 adults, 10 adolescents, and 10 children). In particular, average percentage time in target [70, 180] mg/dl was significantly improved over the evaluated period (first week versus last week): 70.9 ± 11.8 versus 91.1 ± 4.4 (adults), 46.5 ± 11.9 versus 80.1 ± 10.9 (adolescents), 49.4 ± 12.9 versus 73.7 ± 4.1 (children). Average percentage time in hypoglycemia (<70 mg/dl) was also significantly reduced: 9.7 ± 6.6 versus 0.9 ± 1.2 (adults), 10.5 ± 8.3 versus 0.83 ± 1.0 (adolescents), 10.9 ± 6.1 versus 3.2 ± 3.5 (children). When compared against an existing technique over the whole evaluated period, the presented approach achieved superior results on percentage of time in hypoglycemia: 3.9 ± 2.6 versus 2.6 ± 2.2 (adults), 2.9 ± 1.9 versus 2.0 ± 1.5 (adolescents), 4.6 ± 2.8 versus 3.5 ± 2.0 (children), without increasing the percentage time in hyperglycemia. CONCLUSION: The present study shows the potential of a novel technique to effectively adjust the basal insulin profile of a type 1 diabetes population on sensor-augmented insulin pump therapy.
Issue Date: 1-Mar-2018
Date of Acceptance: 5-Feb-2018
URI: http://hdl.handle.net/10044/1/72147
DOI: https://dx.doi.org/10.1177/1932296818761752
ISSN: 1932-2968
Publisher: SAGE Publications
Start Page: 282
End Page: 294
Journal / Book Title: Journal of Diabetes Science and Technology
Volume: 12
Issue: 2
Copyright Statement: © 2018 Diabetes Technology Society. The final, definitive version of this paper has been published inJournal of Diabetes Science and Technology by Sage Publications Ltd. All rights reserved. It is available at: https://journals.sagepub.com/doi/pdf/10.1177/1932296818761752
Sponsor/Funder: Wellcome Trust
Wellcome Trust
Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
Medical Research Council (MRC)
Funder's Grant Number: 089758/Z/09/Z
WT 100921/Z/13/Z
EP/P00993X/1
689810
MC_PC_12015
Keywords: adaptive control
artificial intelligence
basal insulin
case-based reasoning
run-to-run
type 1 diabetes
Adolescent
Adult
Algorithms
Artificial Intelligence
Blood Glucose Self-Monitoring
Child
Diabetes Mellitus, Type 1
Female
Humans
Insulin
Insulin Infusion Systems
Male
Models, Theoretical
Humans
Diabetes Mellitus, Type 1
Insulin
Blood Glucose Self-Monitoring
Insulin Infusion Systems
Algorithms
Models, Theoretical
Artificial Intelligence
Adolescent
Adult
Child
Female
Male
adaptive control
artificial intelligence
basal insulin
case-based reasoning
run-to-run
type 1 diabetes
1111 Nutrition and Dietetics
Publication Status: Published
Conference Place: United States
Online Publication Date: 2018-03-01
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering
Department of Medicine
Faculty of Medicine



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