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Article

Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models

1
Department of Telematic Engineering, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain
2
UC3M-BS Institute of Financial Big Data, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Giovanni Sparacino
Sensors 2021, 21(14), 4926; https://doi.org/10.3390/s21144926
Received: 25 May 2021 / Revised: 14 July 2021 / Accepted: 17 July 2021 / Published: 20 July 2021
(This article belongs to the Section Biomedical Sensors)
Type 1 diabetes is a chronic disease caused by the inability of the pancreas to produce insulin. Patients suffering type 1 diabetes depend on the appropriate estimation of the units of insulin they have to use in order to keep blood glucose levels in range (considering the calories taken and the physical exercise carried out). In recent years, machine learning models have been developed in order to help type 1 diabetes patients with their blood glucose control. These models tend to receive the insulin units used and the carbohydrate taken as inputs and generate optimal estimations for future blood glucose levels over a prediction horizon. The body glucose kinetics is a complex user-dependent process, and learning patient-specific blood glucose patterns from insulin units and carbohydrate content is a difficult task even for deep learning-based models. This paper proposes a novel mechanism to increase the accuracy of blood glucose predictions from deep learning models based on the estimation of carbohydrate digestion and insulin absorption curves for a particular patient. This manuscript proposes a method to estimate absorption curves by using a simplified model with two parameters which are fitted to each patient by using a genetic algorithm. Using simulated data, the results show the ability of the proposed model to estimate absorption curves with mean absolute errors below 0.1 for normalized fast insulin curves having a maximum value of 1 unit. View Full-Text
Keywords: type 1 diabetics; glucose estimation; long short-term memory (LSTM); deep learning; insulin absorption; carbohydrate digestion; artificial intelligence type 1 diabetics; glucose estimation; long short-term memory (LSTM); deep learning; insulin absorption; carbohydrate digestion; artificial intelligence
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MDPI and ACS Style

Muñoz-Organero, M.; Queipo-Álvarez, P.; García Gutiérrez, B. Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models. Sensors 2021, 21, 4926. https://doi.org/10.3390/s21144926

AMA Style

Muñoz-Organero M, Queipo-Álvarez P, García Gutiérrez B. Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models. Sensors. 2021; 21(14):4926. https://doi.org/10.3390/s21144926

Chicago/Turabian Style

Muñoz-Organero, Mario, Paula Queipo-Álvarez, and Boni García Gutiérrez. 2021. "Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models" Sensors 21, no. 14: 4926. https://doi.org/10.3390/s21144926

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