Martinez Delgado, LauraMuñoz Organero, MarioQueipo Alvarez, Paula2021-10-192021-10-192021-08-02Martínez-Delgado, L., Munoz-Organero, M. & Queipo-Alvarez, P. (2021). Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions. Sensors, 21(16), 5273.1424-8220https://hdl.handle.net/10016/33471This article belongs to the Special Issue Sensors, Systems, and AI for Healthcare.Diabetes is a chronic disease caused by the inability of the pancreas to produce insulin or problems in the body to use it efficiently. It is one of the fastest growing health challenges affecting more than 400 million people worldwide, according to the World Health Organization. Intensive research is being carried out on artificial intelligence methods to help people with diabetes to optimize the way in which they use insulin, carbohydrate intakes, or physical activity. By predicting upcoming levels of blood glucose concentrations, preventive actions can be taken. Previous research studies using machine learning methods for blood glucose level predictions have mainly focused on the machine learning model used. Little attention has been given to the pre-processing of insulin and carbohydrate signals in order to mimic the human absorption processes. In this manuscript, a recurrent neural network (RNN) based model for predicting upcoming blood glucose levels in people with type 1 diabetes is combined with several carbohydrate and insulin absorption curves in order to optimize the prediction results. The proposed method is applied to data from real patients suffering type 1 diabetes mellitus (T1DM). The achieved results are encouraging, obtaining accuracy levels around 0.510 mmol/L (9.2 mg/dl) in the best scenario.29eng© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Atribución 3.0 EspañaDeep learningLSTMData processingGlucose level predictionInsulin absorptionCarbohydrate absorptionMachine learning modelsresearch articleTelecomunicacioneshttps://doi.org/10.3390/s21165273open access527316Sensors21AR/0000028481