Innovative approach for online prediction of blood glucose profile in Type 1 diabetes patients

Giovanna Castillo Estrada; Harald Kirchsteiger; Luigi Del Re; Eric Renard

doi:10.1109/acc.2010.5531630

Innovative approach for online prediction of blood glucose profile in Type 1 diabetes patients

Giovanna Castillo Estrada
, Harald Kirchsteiger
, Luigi Del Re
, Eric Renard

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

18 Citations (Scopus)

Abstract

Recursive identification techniques are used to estimate predictions for the human glucose-insulin subsystem. By replacing a constant gain with a physiologically inspired adaptation rule and adding as additional inputs the two variables ingested meal and administered insulin-which have the highest impact on the glucose concentration-the overall performance of a 45 min glucose prediction could be increased compared to standard identification and prediction methods. The results were analyzed from a system theoretical, and also from a clinical point of view using the CG-EGA.

Original language	English
Title of host publication	Proceedings of the 2010 American Control Conference, ACC 2010
Publisher	IEEE Computer Society
Pages	2015-2020
Number of pages	6
ISBN (Print)	9781424474264
DOIs	https://doi.org/10.1109/acc.2010.5531630
Publication status	Published - 2010
Externally published	Yes

Publication series

Name	Proceedings of the 2010 American Control Conference, ACC 2010

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1109/acc.2010.5531630

Cite this

Estrada, G. C., Kirchsteiger, H., Del Re, L., & Renard, E. (2010). Innovative approach for online prediction of blood glucose profile in Type 1 diabetes patients. In Proceedings of the 2010 American Control Conference, ACC 2010 (pp. 2015-2020). Article 5531630 (Proceedings of the 2010 American Control Conference, ACC 2010). IEEE Computer Society. https://doi.org/10.1109/acc.2010.5531630

@inproceedings{063fb93bdce14e9085dc8b7385c48404,

title = "Innovative approach for online prediction of blood glucose profile in Type 1 diabetes patients",

abstract = "Recursive identification techniques are used to estimate predictions for the human glucose-insulin subsystem. By replacing a constant gain with a physiologically inspired adaptation rule and adding as additional inputs the two variables ingested meal and administered insulin-which have the highest impact on the glucose concentration-the overall performance of a 45 min glucose prediction could be increased compared to standard identification and prediction methods. The results were analyzed from a system theoretical, and also from a clinical point of view using the CG-EGA.",

author = "Estrada, \{Giovanna Castillo\} and Harald Kirchsteiger and \{Del Re\}, Luigi and Eric Renard",

note = "Funding Information: The FLEMENGHO Study was supported by research grants G.0174.97 and G.0291.98 from the Fonds voor Wetenschappelijk Onderzoek Vlaanderen (Brussels, Belgium), the Bilaterale Wetenschappelijke en Technische Samenwerking Vlaanderen-China (project BIL98/15), and a special research grant from the Katholieke Universiteit Leuven (Onderzoekstoelage OT99/28). Families were recruited in the framework of the European Project on Genes in Hypertension, which is partially financed by the European Union (contract number IC15-CT98-0329-EPOGH). The epidemiologic work in Belgium and the genetic research in Milan, Italy were conducted as part of the EURNETGEN project (EU grant number QLG1-CT-2000-01137). The genetic laboratory also received grants from the Ministero della Sanit{\`a} to Giuseppe Bianchi (RF200-00-49 and MM06A9241-001) and the Ministero Universit{\`a} e Ricerca Scientifica of Italy to Cristina Barlassina (ex MPI 60\% 2000). The FLEMENGHO study would not have been possible without the collaboration of the family physicians of the participants. The municipality Hechtel-Eksel (Belgium) provided logistic support. The authors acknowledge the expert assistance of Leszek Bieniaszewski, Rina Bollen, Lut De Pauw, Hilde Celis, Elly Den Hond, Primoz Dolenc, Paul Drent, Dmitri Emelianov, Jerzy G{\c a}sowski, Heng Fan, Lieve Gijsbers, Alida Hermans, Tim Nawrot, Lutgarde Thijs, Yvette Toremans, Sylvia Van Hulle, and Renilde Wolfs (Study Coordinating Centre, Leuven, Belgium).",

year = "2010",

doi = "10.1109/acc.2010.5531630",

language = "English",

isbn = "9781424474264",

series = "Proceedings of the 2010 American Control Conference, ACC 2010",

publisher = "IEEE Computer Society",

pages = "2015--2020",

booktitle = "Proceedings of the 2010 American Control Conference, ACC 2010",

address = "United States",

}

Estrada, GC, Kirchsteiger, H, Del Re, L & Renard, E 2010, Innovative approach for online prediction of blood glucose profile in Type 1 diabetes patients. in Proceedings of the 2010 American Control Conference, ACC 2010., 5531630, Proceedings of the 2010 American Control Conference, ACC 2010, IEEE Computer Society, pp. 2015-2020. https://doi.org/10.1109/acc.2010.5531630

Innovative approach for online prediction of blood glucose profile in Type 1 diabetes patients. / Estrada, Giovanna Castillo; Kirchsteiger, Harald; Del Re, Luigi et al.
Proceedings of the 2010 American Control Conference, ACC 2010. IEEE Computer Society, 2010. p. 2015-2020 5531630 (Proceedings of the 2010 American Control Conference, ACC 2010).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

TY - GEN

T1 - Innovative approach for online prediction of blood glucose profile in Type 1 diabetes patients

AU - Estrada, Giovanna Castillo

AU - Kirchsteiger, Harald

AU - Del Re, Luigi

AU - Renard, Eric

N1 - Funding Information: The FLEMENGHO Study was supported by research grants G.0174.97 and G.0291.98 from the Fonds voor Wetenschappelijk Onderzoek Vlaanderen (Brussels, Belgium), the Bilaterale Wetenschappelijke en Technische Samenwerking Vlaanderen-China (project BIL98/15), and a special research grant from the Katholieke Universiteit Leuven (Onderzoekstoelage OT99/28). Families were recruited in the framework of the European Project on Genes in Hypertension, which is partially financed by the European Union (contract number IC15-CT98-0329-EPOGH). The epidemiologic work in Belgium and the genetic research in Milan, Italy were conducted as part of the EURNETGEN project (EU grant number QLG1-CT-2000-01137). The genetic laboratory also received grants from the Ministero della Sanità to Giuseppe Bianchi (RF200-00-49 and MM06A9241-001) and the Ministero Università e Ricerca Scientifica of Italy to Cristina Barlassina (ex MPI 60% 2000). The FLEMENGHO study would not have been possible without the collaboration of the family physicians of the participants. The municipality Hechtel-Eksel (Belgium) provided logistic support. The authors acknowledge the expert assistance of Leszek Bieniaszewski, Rina Bollen, Lut De Pauw, Hilde Celis, Elly Den Hond, Primoz Dolenc, Paul Drent, Dmitri Emelianov, Jerzy Gąsowski, Heng Fan, Lieve Gijsbers, Alida Hermans, Tim Nawrot, Lutgarde Thijs, Yvette Toremans, Sylvia Van Hulle, and Renilde Wolfs (Study Coordinating Centre, Leuven, Belgium).

PY - 2010

Y1 - 2010

N2 - Recursive identification techniques are used to estimate predictions for the human glucose-insulin subsystem. By replacing a constant gain with a physiologically inspired adaptation rule and adding as additional inputs the two variables ingested meal and administered insulin-which have the highest impact on the glucose concentration-the overall performance of a 45 min glucose prediction could be increased compared to standard identification and prediction methods. The results were analyzed from a system theoretical, and also from a clinical point of view using the CG-EGA.

AB - Recursive identification techniques are used to estimate predictions for the human glucose-insulin subsystem. By replacing a constant gain with a physiologically inspired adaptation rule and adding as additional inputs the two variables ingested meal and administered insulin-which have the highest impact on the glucose concentration-the overall performance of a 45 min glucose prediction could be increased compared to standard identification and prediction methods. The results were analyzed from a system theoretical, and also from a clinical point of view using the CG-EGA.

UR - https://www.scopus.com/pages/publications/77957815223

U2 - 10.1109/acc.2010.5531630

DO - 10.1109/acc.2010.5531630

M3 - Conference contribution

AN - SCOPUS:77957815223

SN - 9781424474264

T3 - Proceedings of the 2010 American Control Conference, ACC 2010

SP - 2015

EP - 2020

BT - Proceedings of the 2010 American Control Conference, ACC 2010

PB - IEEE Computer Society

ER -

Innovative approach for online prediction of blood glucose profile in Type 1 diabetes patients

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