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Automatic adaptation of Basal insulin using sensor-augmented pump therapy
File | Description | Size | Format | |
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HERRERO JDST_2018.pdf | Accepted version | 1.42 MB | Adobe PDF | View/Open |
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: | Electrical and Electronic Engineering Department of Medicine (up to 2019) Faculty of Engineering |