Imperial College London
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Professor Pantelis Georgiou

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Biomedical Electronics
 
 
 
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Contact

 

+44 (0)20 7594 6326pantelis Website

 
 
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Location

 

902Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Guemes:2020:10.1109/JBHI.2019.2938305,
author = {Guemes, A and Cappon, G and Hernandez, B and Reddy, M and Oliver, N and Georgiou, P and Herrero, P},
doi = {10.1109/JBHI.2019.2938305},
journal = {IEEE Journal of Biomedical and Health Informatics},
pages = {1439--1446},
title = {Predicting quality of overnight glycaemic control in type 1 diabetes using binary classifiers},
url = {http://dx.doi.org/10.1109/JBHI.2019.2938305},
volume = {24},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challenging. Although semi-automatic systems to modulate insulin pump delivery, such as low-glucose insulin suspension and the artificial pancreas, are starting to become a reality, their elevated cost and performance below user expectations is hindering their adoption. Hence, a decision support system that helps people with type 1 diabetes, on multiple daily injections or insulin pump therapy, to avoid undesirable overnight blood glucose fluctuations (hyper- or hypoglycaemic) is an attractive alternative. In this paper, we introduce a novel data-driven approach to predict the quality of overnight glycaemic control in people with type 1 diabetes by analyzing commonly gathered data during the day-time period (continuous glucose monitoring data, meal intake and insulin boluses). The proposed approach is able to predict whether overnight blood glucose concentrations are going to remain within or outside the target range, and therefore allows the user to take the appropriate preventive action (snack or change in basal insulin). For this purpose, a number of popular established machine learning algorithms for classification were evaluated and compared on a publicly available clinical dataset (i.e. OhioT1DM). Although there is no clearly superior classification algorithm, this study indicates that, by using commonly gathered data in type 1 diabetes management, it is possible to predict the quality of overnight glycaemic control with reasonable accuracy (AUC-ROC= 0.7).
AU  - Guemes,A
AU  - Cappon,G
AU  - Hernandez,B
AU  - Reddy,M
AU  - Oliver,N
AU  - Georgiou,P
AU  - Herrero,P
DO  - 10.1109/JBHI.2019.2938305
EP  - 1446
PY  - 2020///
SN  - 2168-2194
SP  - 1439
TI  - Predicting quality of overnight glycaemic control in type 1 diabetes using binary classifiers
T2  - IEEE Journal of Biomedical and Health Informatics
UR  - http://dx.doi.org/10.1109/JBHI.2019.2938305
UR  - https://www.ncbi.nlm.nih.gov/pubmed/31536025
UR  - https://ieeexplore.ieee.org/document/8836640
UR  - http://hdl.handle.net/10044/1/73798
VL  - 24
ER  -
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