Paper Info
Title
Predicting the meal macronutrient composition from continuous glucose monitors
Abstract
Sustained high levels of blood glucose in type 2 diabetes (T2DM) can have disastrous long-term health consequences. An essential component of clinical interventions for T2DM is monitoring dietary intake to keep plasma glucose levels within an acceptable range. Yet, current techniques to monitor food intake are time intensive and error prone. To address this issue, we are developing techniques to automatically monitor food intake and the composition of those foods using continuous glucose monitors (CGMs). This article presents the results of a clinical study in which participants consumed nine standardized meals with known macronutrients amounts (carbohydrate, protein, and fat) while wearing a CGM. We built a multitask neural network to estimate the macronutrient composition from the CGM signal, and compared it against a baseline linear regression. The best prediction result comes from our proposed neural network, trained with subject-dependent data, as measured by root mean squared relative error and correlation coefficient. These findings suggest that it is possible to estimate macronutrient composition from CGM signals, opening the possibility to develop automatic techniques to track food intake.
Year
DOI
Venue
2019
10.1109/BHI.2019.8834488
2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Keywords
Field
DocType
Continuous Glucose Monitoring,multitask learning,meal composition prediction,neural networks
Correlation coefficient,Continuous glucose monitoring,Type 2 diabetes,Meal,Clinical study,Glucose monitors,Statistics,Medicine,Linear regression
Conference
ISSN
ISBN
Citations 
2641-3590
978-1-7281-0849-0
0
PageRank 
References 
Authors
0.34
9
6
Name
Order
Citations
PageRank
Zepeng Huo121.03
Bobak J. Mortazavi202.70
Theodora Chaspari33819.43
Nicolaas E. P. Deutz401.01
Laura Ruebush501.01
Ricardo Gutierrez-Osuna636544.59