RT Journal Article
T1 Learning carbohydrate digestion and insulin absorption curves using blood glucose level prediction and deep learning models
A1 Muñoz Organero, Mario
A1 Queipo Alvarez, Paula
A1 García Gutiérrez, Boni
AB 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.
PB MDPI
SN 1424-8220
YR 2021
FD 2021-07-02
LK https://hdl.handle.net/10016/33470
UL https://hdl.handle.net/10016/33470
LA eng
NO This article belongs to the Section Biomedical Sensors.
NO This work was supported in part by the project "ANALISIS EN TIEMPO REAL DE SENSORES SOCIALES Y ESTIMACION DE RECURSOS PARA TRANSPORTE MULTIMODAL BASADA EN APRENDIZAJE PROFUNDO" MaGIST-RALES, funded by the Spanish Agencia Estatal de Investigación (AEI, doi 10.13039/501100011033) under grant PID2019-105221RB-C44 /AEI/10.13039/501100011033. This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M21), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).
DS e-Archivo
RD 18 may. 2024