Glycaemic index prediction: a pilot study of data linkage challenges and the application of machine learning

Jingyuan Li; Ognjen Arandelovic

doi:10.1109/BHI.2017.7897279

Glycaemic index prediction: a pilot study of data linkage challenges and the application of machine learning

Jingyuan Li, Ognjen Arandelovic

School of Computer Science

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

Abstract

The glycaemic index (GI) is widely used to characterize the effect that a food has on blood glucose which is of major importance to diabetic individuals as well as the general population at large. At present, its applicability is severely limited by the labour involved in its measurement and the lack of understanding about how different foods interact to produce the GI of the meal comprising them. In this pilot study we examine if readily available biochemical properties of food scan be used to predict their GI, thus opening possibilities for practicable use of the GI in the management of blood glucose in everyday life. We also examine practical challenges in the cross-linking of food information sources collected by different organizations, and highlight the need for the development of a universal standard which would facilitate automatic and error free data integration.

Original language	English
Title of host publication	2017 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
Publisher	IEEE
Pages	357-360
ISBN (Electronic)	9781509041794, 9781509041800
DOIs	https://doi.org/10.1109/BHI.2017.7897279
Publication status	Published - 18 Feb 2017
Event	BHI-2017 International Conference on Biomedical and Health Informatics - Rosen Plaza Hotel, Orlando, United States Duration: 16 Feb 2017 → 19 Feb 2017 Conference number: 4 http://bhi.embs.org/2017/

Conference

Conference	BHI-2017 International Conference on Biomedical and Health Informatics
Abbreviated title	BHI
Country/Territory	United States
City	Orlando
Period	16/02/17 → 19/02/17
Internet address	http://bhi.embs.org/2017/

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10.1109/BHI.2017.7897279

Li_Glycaemic_BHI2017_AAM
© 2017, IEEE. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at ieeexplore.ieee.org / https://doi.org/10.1109/BHI.2017.7897279
Accepted author manuscript, 292 KB

Cite this

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title = "Glycaemic index prediction: a pilot study of data linkage challenges and the application of machine learning",

abstract = "The glycaemic index (GI) is widely used to characterize the effect that a food has on blood glucose which is of major importance to diabetic individuals as well as the general population at large. At present, its applicability is severely limited by the labour involved in its measurement and the lack of understanding about how different foods interact to produce the GI of the meal comprising them. In this pilot study we examine if readily available biochemical properties of food scan be used to predict their GI, thus opening possibilities for practicable use of the GI in the management of blood glucose in everyday life. We also examine practical challenges in the cross-linking of food information sources collected by different organizations, and highlight the need for the development of a universal standard which would facilitate automatic and error free data integration.",

author = "Jingyuan Li and Ognjen Arandelovic",

year = "2017",

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day = "18",

doi = "10.1109/BHI.2017.7897279",

language = "English",

pages = "357--360",

booktitle = "2017 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)",

publisher = "IEEE",

note = "BHI-2017 International Conference on Biomedical and Health Informatics, BHI ; Conference date: 16-02-2017 Through 19-02-2017",

url = "http://bhi.embs.org/2017/",

}

Li, J & Arandelovic, O 2017, Glycaemic index prediction: a pilot study of data linkage challenges and the application of machine learning. in 2017 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)., 7897279, IEEE, pp. 357-360, BHI-2017 International Conference on Biomedical and Health Informatics, Orlando, Florida, United States, 16/02/17. https://doi.org/10.1109/BHI.2017.7897279

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T2 - BHI-2017 International Conference on Biomedical and Health Informatics

AU - Li, Jingyuan

AU - Arandelovic, Ognjen

N1 - Conference code: 4

PY - 2017/2/18

Y1 - 2017/2/18

N2 - The glycaemic index (GI) is widely used to characterize the effect that a food has on blood glucose which is of major importance to diabetic individuals as well as the general population at large. At present, its applicability is severely limited by the labour involved in its measurement and the lack of understanding about how different foods interact to produce the GI of the meal comprising them. In this pilot study we examine if readily available biochemical properties of food scan be used to predict their GI, thus opening possibilities for practicable use of the GI in the management of blood glucose in everyday life. We also examine practical challenges in the cross-linking of food information sources collected by different organizations, and highlight the need for the development of a universal standard which would facilitate automatic and error free data integration.

AB - The glycaemic index (GI) is widely used to characterize the effect that a food has on blood glucose which is of major importance to diabetic individuals as well as the general population at large. At present, its applicability is severely limited by the labour involved in its measurement and the lack of understanding about how different foods interact to produce the GI of the meal comprising them. In this pilot study we examine if readily available biochemical properties of food scan be used to predict their GI, thus opening possibilities for practicable use of the GI in the management of blood glucose in everyday life. We also examine practical challenges in the cross-linking of food information sources collected by different organizations, and highlight the need for the development of a universal standard which would facilitate automatic and error free data integration.

U2 - 10.1109/BHI.2017.7897279

DO - 10.1109/BHI.2017.7897279

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BT - 2017 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

PB - IEEE

Y2 - 16 February 2017 through 19 February 2017

ER -

Glycaemic index prediction: a pilot study of data linkage challenges and the application of machine learning

Abstract

Conference

UN SDGs

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