Utility of big data in predicting short-term blood glucose levels in type 1 diabetes mellitus through machine learning techniques
Author
Rodríguez Rodríguez, Ignacio; Chatzigiannakis, Ioannis; Rodríguez Rodríguez, José Víctor; Maranghi, Marianna; Gentili, Michele; [et al.]Knowledge Area
Ingeniería EléctricaIngeniería QuímicaQuímica-FísicaSponsors
The authors would like to thank to the Endocrinology Department of the Morales Meseguer and Virgen de la Arrixaca hospitals of the city of Murcia (Spain). This work was sponsored by the Spanish Ministry of Economy and Competitiveness through 387 the PERSEIDES (ref. TIN2017-86885-R) and CHIST-ERA (ref. PCIN-2016-010) projects; by MINECO grant BES-2015-071956, and by the European Comission through the H2020-ENTROPY-649849 EU Project.Realizado en/con
Universidad Politécnica de Cartagena; Universidad de Murcia; Sapienza University RomePublication date
2019-10-16Publisher
MDPIBibliographic Citation
Rodríguez-Rodríguez I, Chatzigiannakis I, Rodríguez J-V, Maranghi M, Gentili M, Zamora-Izquierdo M-Á. Utility of Big Data in Predicting Short-Term Blood Glucose Levels in Type 1 Diabetes Mellitus Through Machine Learning Techniques. Sensors. 2019; 19(20):4482. https://doi.org/10.3390/s19204482Peer review
SiKeywords
Continuous glucose monitoringWearable devices
Short-term prediction
Univariate time series
Machine learning
Experimental evaluation
Abstract
Machine learning techniques combined with wearable electronics can deliver accurate short-term blood glucose level prediction models. These models can learn personalized glucose–insulin dynamics based on the sensor data collected by monitoring several aspects of the physiological condition and daily activity of an individual. Until now, the prevalent approach for developing
data-driven prediction models was to collect as much data as possible to help physicians and patients optimally adjust therapy. The objective of this work was to investigate the minimum data variety, volume, and velocity required to create accurate person-centric short-term prediction models. We developed a series of these models using different machine learning time series forecasting techniques suitable for execution within a wearable processor. We conducted an extensive passive patient monitoring study in real-world conditions to build an appropriate data set. The study involved a subset of type 1 diabetic subjects ...
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