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

^*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

6 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Artificial Intelligence in Medicine - 12th Conference on Artificial Intelligence in Medicine, AIME 2009, Proceedings
Páginas	295-304
Número de páginas	10
DOI	https://doi.org/10.1007/978-3-642-02976-9_42
Estado	Publicada - 2009
Publicado de forma externa	Sí
Evento	12th Conference on Artificial Intelligence in Medicine, AIME 2009 - Verona, Italia Duración: 18 jul 2009 → 22 jul 2009

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	5651 LNAI
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	12th Conference on Artificial Intelligence in Medicine, AIME 2009
País/Territorio	Italia
Ciudad	Verona
Período	18/07/09 → 22/07/09

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 3: Salud y bienestar

Acceder al documento

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

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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. En 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",

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

pages = "295--304",

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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. En 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, Italia, 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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. En 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

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