Citation

BibTex format

@article{Herrero:2015:10.1109/JBHI.2014.2331896,
author = {Herrero, P and Pesl, P and Reddy, M and Oliver, N and Georgiou, P and Toumazou, C},
doi = {10.1109/JBHI.2014.2331896},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1087--1096},
title = {Advanced insulin bolus advisor based on run-to-run control and case-based reasoning},
url = {http://dx.doi.org/10.1109/JBHI.2014.2331896},
volume = {19},
year = {2015}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - This paper presents an advanced insulin bolus advisor for people with diabetes on multiple daily injections or insulin pump therapy. The proposed system, which runs on a smartphone, keeps the simplicity of a standard bolus calculator while enhancing its performance by providing more adaptability and flexibility. This is achieved by means of applying a retrospective optimization of the insulin bolus therapy using a novel combination of run-to-run (R2R) that uses intermittent continuous glucose monitoring data, and case-based reasoning (CBR). The validity of the proposed approach has been proven by in-silico studies using the FDA-accepted UVa-Padova type 1 diabetes simulator. Tests under more realistic in-silico scenarios are achieved by updating the simulator to emulate intrasubject insulin sensitivity variations and uncertainty in the capillarity measurements and carbohydrate intake. The CBR(R2R) algorithm performed well in simulations by significantly reducing the mean blood glucose, increasing the time in euglycemia and completely eliminating hypoglycaemia. Finally, compared to an R2R stand-alone version of the algorithm, the CBR(R2R) algorithm performed better in both adults and adolescent populations, proving the benefit of the utilization of CBR. In particular, the mean blood glucose improved from 166 ± 39 to 150 ± 16 in the adult populations (p = 0.03) and from 167 ± 25 to 162 ± 23 in the adolescent population (p = 0.06). In addition, CBR(R2R) was able to completely eliminate hypoglycaemia, while the R2R alone was not able to do it in the adolescent population.
AU - Herrero,P
AU - Pesl,P
AU - Reddy,M
AU - Oliver,N
AU - Georgiou,P
AU - Toumazou,C
DO - 10.1109/JBHI.2014.2331896
EP - 1096
PY - 2015///
SN - 2168-2194
SP - 1087
TI - Advanced insulin bolus advisor based on run-to-run control and case-based reasoning
T2 - IEEE Journal of Biomedical and Health Informatics
UR - http://dx.doi.org/10.1109/JBHI.2014.2331896
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000356511900036&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://ieeexplore.ieee.org/document/6838970
VL - 19
ER -

Contact us

Centre for Bio-Inspired Technology
Imperial College London
Bessemer Building
South Kensington
SW7 2AZ, UK

Tel: +44 (0)207 594 0701
Fax: +44 (0)207 594 0704

E-mail: bioinspired@imperial.ac.uk