Mealtime blood glucose classifier based on fuzzy logic for the DIABTel telemedicine system

Gema García-Sáez; José M. Alonso; Javier Molero; Mercedes Rigla; Iñaki Martínez-Sarriegui; Alberto De Leiva; Enrique J. Gómez; M. Elena Hernando

doi:10.1007/978-3-642-02976-9_42

Mealtime blood glucose classifier based on fuzzy logic for the DIABTel telemedicine system

Gema García-Sáez^*
, José M. Alonso
, Javier Molero
, Mercedes Rigla
, Iñaki Martínez-Sarriegui
, Alberto De Leiva
, Enrique J. Gómez
, M. Elena Hernando

^*Corresponding author for this work

Technical University of Madrid
European Centre for Soft Computing
Hospital de Sabadell

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

6 Citations (Scopus)

Abstract

The accurate interpretation of Blood Glucose (BG) values is essential for diabetes care. However, BG monitoring data does not provide complete information about associated meal and moment of measurement, unless patients fulfil it manually. An automatic classification of incomplete BG data helps to a more accurate interpretation, contributing to Knowledge Management (KM) tools that support decision-making in a telemedicine system. This work presents a fuzzy rule-based classifier integrated in a KM agent of the DIABTel telemedicine architecture, to automatically classify BG measurements into meal intervals and moments of measurement. Fuzzy Logic (FL) tackles with the incompleteness of BG measurements and provides a semantic expressivity quite close to natural language used by physicians, what makes easier the system output interpretation. The best mealtime classifier provides an accuracy of 77.26% and does not increase significantly the KM analysis times. Results of classification are used to extract anomalous trends in the patient's data.

Original language	English
Title of host publication	Artificial Intelligence in Medicine - 12th Conference on Artificial Intelligence in Medicine, AIME 2009, Proceedings
Pages	295-304
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-02976-9_42
Publication status	Published - 2009
Externally published	Yes
Event	12th Conference on Artificial Intelligence in Medicine, AIME 2009 - Verona, Italy Duration: 18 Jul 2009 → 22 Jul 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5651 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th Conference on Artificial Intelligence in Medicine, AIME 2009
Country/Territory	Italy
City	Verona
Period	18/07/09 → 22/07/09

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Classification
Diabetes
Fuzzy Logic
Telemedicine

Access to Document

10.1007/978-3-642-02976-9_42

Cite this

García-Sáez, G., Alonso, J. M., Molero, J., Rigla, M., Martínez-Sarriegui, I., De Leiva, A., Gómez, E. J., & Hernando, M. E. (2009). Mealtime blood glucose classifier based on fuzzy logic for the DIABTel telemedicine system. In Artificial Intelligence in Medicine - 12th Conference on Artificial Intelligence in Medicine, AIME 2009, Proceedings (pp. 295-304). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5651 LNAI). https://doi.org/10.1007/978-3-642-02976-9_42

García-Sáez, Gema ; Alonso, José M. ; Molero, Javier et al. / Mealtime blood glucose classifier based on fuzzy logic for the DIABTel telemedicine system. Artificial Intelligence in Medicine - 12th Conference on Artificial Intelligence in Medicine, AIME 2009, Proceedings. 2009. pp. 295-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2782d6146682444da743db3f48046046,

title = "Mealtime blood glucose classifier based on fuzzy logic for the DIABTel telemedicine system",

abstract = "The accurate interpretation of Blood Glucose (BG) values is essential for diabetes care. However, BG monitoring data does not provide complete information about associated meal and moment of measurement, unless patients fulfil it manually. An automatic classification of incomplete BG data helps to a more accurate interpretation, contributing to Knowledge Management (KM) tools that support decision-making in a telemedicine system. This work presents a fuzzy rule-based classifier integrated in a KM agent of the DIABTel telemedicine architecture, to automatically classify BG measurements into meal intervals and moments of measurement. Fuzzy Logic (FL) tackles with the incompleteness of BG measurements and provides a semantic expressivity quite close to natural language used by physicians, what makes easier the system output interpretation. The best mealtime classifier provides an accuracy of 77.26\% and does not increase significantly the KM analysis times. Results of classification are used to extract anomalous trends in the patient's data.",

keywords = "Classification, Diabetes, Fuzzy Logic, Telemedicine",

author = "Gema Garc{\'i}a-S{\'a}ez and Alonso, \{Jos{\'e} M.\} and Javier Molero and Mercedes Rigla and I{\~n}aki Mart{\'i}nez-Sarriegui and \{De Leiva\}, Alberto and G{\'o}mez, \{Enrique J.\} and Hernando, \{M. Elena\}",

year = "2009",

doi = "10.1007/978-3-642-02976-9\_42",

language = "English",

isbn = "3642029752",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "295--304",

booktitle = "Artificial Intelligence in Medicine - 12th Conference on Artificial Intelligence in Medicine, AIME 2009, Proceedings",

note = "12th Conference on Artificial Intelligence in Medicine, AIME 2009 ; Conference date: 18-07-2009 Through 22-07-2009",

}

García-Sáez, G, Alonso, JM, Molero, J, Rigla, M, Martínez-Sarriegui, I, De Leiva, A, Gómez, EJ & Hernando, ME 2009, Mealtime blood glucose classifier based on fuzzy logic for the DIABTel telemedicine system. in Artificial Intelligence in Medicine - 12th Conference on Artificial Intelligence in Medicine, AIME 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5651 LNAI, pp. 295-304, 12th Conference on Artificial Intelligence in Medicine, AIME 2009, Verona, Italy, 18/07/09. https://doi.org/10.1007/978-3-642-02976-9_42

Mealtime blood glucose classifier based on fuzzy logic for the DIABTel telemedicine system. / García-Sáez, Gema; Alonso, José M.; Molero, Javier et al.
Artificial Intelligence in Medicine - 12th Conference on Artificial Intelligence in Medicine, AIME 2009, Proceedings. 2009. p. 295-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5651 LNAI).

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

TY - GEN

T1 - Mealtime blood glucose classifier based on fuzzy logic for the DIABTel telemedicine system

AU - García-Sáez, Gema

AU - Alonso, José M.

AU - Molero, Javier

AU - Rigla, Mercedes

AU - Martínez-Sarriegui, Iñaki

AU - De Leiva, Alberto

AU - Gómez, Enrique J.

AU - Hernando, M. Elena

PY - 2009

Y1 - 2009

N2 - The accurate interpretation of Blood Glucose (BG) values is essential for diabetes care. However, BG monitoring data does not provide complete information about associated meal and moment of measurement, unless patients fulfil it manually. An automatic classification of incomplete BG data helps to a more accurate interpretation, contributing to Knowledge Management (KM) tools that support decision-making in a telemedicine system. This work presents a fuzzy rule-based classifier integrated in a KM agent of the DIABTel telemedicine architecture, to automatically classify BG measurements into meal intervals and moments of measurement. Fuzzy Logic (FL) tackles with the incompleteness of BG measurements and provides a semantic expressivity quite close to natural language used by physicians, what makes easier the system output interpretation. The best mealtime classifier provides an accuracy of 77.26% and does not increase significantly the KM analysis times. Results of classification are used to extract anomalous trends in the patient's data.

AB - The accurate interpretation of Blood Glucose (BG) values is essential for diabetes care. However, BG monitoring data does not provide complete information about associated meal and moment of measurement, unless patients fulfil it manually. An automatic classification of incomplete BG data helps to a more accurate interpretation, contributing to Knowledge Management (KM) tools that support decision-making in a telemedicine system. This work presents a fuzzy rule-based classifier integrated in a KM agent of the DIABTel telemedicine architecture, to automatically classify BG measurements into meal intervals and moments of measurement. Fuzzy Logic (FL) tackles with the incompleteness of BG measurements and provides a semantic expressivity quite close to natural language used by physicians, what makes easier the system output interpretation. The best mealtime classifier provides an accuracy of 77.26% and does not increase significantly the KM analysis times. Results of classification are used to extract anomalous trends in the patient's data.

KW - Classification

KW - Diabetes

KW - Fuzzy Logic

KW - Telemedicine

UR - https://www.scopus.com/pages/publications/70350212965

U2 - 10.1007/978-3-642-02976-9_42

DO - 10.1007/978-3-642-02976-9_42

M3 - Conference contribution

AN - SCOPUS:70350212965

SN - 3642029752

SN - 9783642029752

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 295

EP - 304

BT - Artificial Intelligence in Medicine - 12th Conference on Artificial Intelligence in Medicine, AIME 2009, Proceedings

T2 - 12th Conference on Artificial Intelligence in Medicine, AIME 2009

Y2 - 18 July 2009 through 22 July 2009

ER -

García-Sáez G, Alonso JM, Molero J, Rigla M, Martínez-Sarriegui I, De Leiva A et al. Mealtime blood glucose classifier based on fuzzy logic for the DIABTel telemedicine system. In Artificial Intelligence in Medicine - 12th Conference on Artificial Intelligence in Medicine, AIME 2009, Proceedings. 2009. p. 295-304. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-02976-9_42

Mealtime blood glucose classifier based on fuzzy logic for the DIABTel telemedicine system

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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