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dc.contributor.authorRodríguez Rodríguez, Ignacio 
dc.contributor.authorChatzigiannakis, Ioannis 
dc.contributor.authorRodríguez Rodríguez, José Víctor 
dc.contributor.authorMaranghi, Marianna 
dc.contributor.authorGentili, Michele 
dc.contributor.authorZamora Izquierdo, Miguel Ángel es_ES
dc.identifier.citationRodríguez-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.
dc.description.abstractMachine 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 wearing a flash glucose monitoring system. We comparatively and quantitatively evaluated the performance of the developed data-driven prediction models and the corresponding machine learning techniques. Our results indicate that very accurate short-term prediction can be achieved by only monitoring interstitial glucose data over a very short time period and using a low sampling frequency. The models developed can predict glucose levels within a 15-min horizon with an average error as low as 15.43 mg/dL using only 24 historic values collected within a period of sex hours, and by increasing the sampling frequency to include 72 values, the average error is reduced to 10.15 mg/dL. Our prediction models are suitable for execution within a wearable device, requiring the minimum hardware requirements while at simultaneously achieving very high prediction accuracy.es_ES
dc.description.sponsorshipThe 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.es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.titleUtility of big data in predicting short-term blood glucose levels in type 1 diabetes mellitus through machine learning techniqueses_ES
dc.subjectContinuous glucose monitoringes_ES
dc.subjectWearable deviceses_ES
dc.subjectShort-term predictiones_ES
dc.subjectUnivariate time serieses_ES
dc.subjectMachine learninges_ES
dc.subjectExperimental evaluationes_ES
dc.subject.otherIngeniería Eléctricaes_ES
dc.subject.otherIngeniería Químicaes_ES
dc.subject.unesco23 Químicaes_ES
dc.subject.unesco3311 Tecnología de la Instrumentaciónes_ES
dc.subject.unesco3306 Ingeniería y Tecnología Eléctricases_ES
dc.contributor.convenianteUniversidad Politécnica de Cartagenaes_ES
dc.contributor.convenianteUniversidad de Murciaes_ES
dc.contributor.convenianteSapienza University Romees_ES

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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España