Deep physiological model for blood glucose prediction in T1DM patients

e-Archivo Repository

Show simple item record

dc.contributor.author Muñoz Organero, Mario
dc.date.accessioned 2021-02-25T10:35:06Z
dc.date.available 2021-02-25T10:35:06Z
dc.date.issued 2020-07-13
dc.identifier.bibliographicCitation Sensors, 20(14), 3896, July 2020, 17 pp.
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/10016/32022
dc.description.abstract Accurate estimations for the near future levels of blood glucose are crucial for Type 1 Diabetes Mellitus (T1DM) patients in order to be able to react on time and avoid hypo and hyper-glycemic episodes. Accurate predictions for blood glucose are the base for control algorithms in glucose regulating systems such as the artificial pancreas. Numerous research studies have already been conducted in order to provide predictions for blood glucose levels with particularities in the input signals and underlying models used. These models can be categorized into two major families: those based on tuning glucose physiological-metabolic models and those based on learning glucose evolution patterns based on machine learning techniques. This paper reviews the state of the art in blood glucose predictions for T1DM patients and proposes, implements, validates and compares a new hybrid model that decomposes a deep machine learning model in order to mimic the metabolic behavior of physiological blood glucose methods. The differential equations for carbohydrate and insulin absorption in physiological models are modeled using a Recurrent Neural Network (RNN) implemented using Long Short-Term Memory (LSTM) cells. The results show Root Mean Square Error (RMSE) values under 5 mg/dL for simulated patients and under 10 mg/dL for real patients.
dc.description.sponsorship This work was supported by the "ANALYTICS USING SENSOR DATA FOR FLATCITY"; Project (MINECO/ERDF, EU) funded in part by the Spanish Agencia Estatal de Investigación (AEI) under Grant TIN2016-77158-C4-1-R and in part by the European Regional Development Fund (ERDF).
dc.format.extent 17
dc.language.iso eng
dc.publisher MDPI
dc.rights © 2020 by the author. Licensee MDPI, Basel, Switzerland.
dc.rights This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Blood glucose prediction
dc.subject.other Type 1 Diabetes mellitus
dc.subject.other Deep machine learning
dc.subject.other Physiological models
dc.title Deep physiological model for blood glucose prediction in T1DM patients
dc.type article
dc.subject.eciencia Biología y Biomedicina
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.3390/s20143896
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2016-77158-C4-1-R
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 14, 3896
dc.identifier.publicationlastpage 17
dc.identifier.publicationtitle SENSORS
dc.identifier.publicationvolume 20
dc.identifier.uxxi AR/0000026214
dc.contributor.funder Ministerio de Economía y Competitividad (España)
 Find Full text

Files in this item

*Click on file's image for preview. (Embargoed files's preview is not supported)


The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record