Utility of big data in predicting short-term blood glucose levels in type 1 diabetes mellitus through machine learning techniques
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AutorRodríguez Rodríguez, Ignacio; Chatzigiannakis, Ioannis; Rodríguez Rodríguez, José Víctor; Maranghi, Marianna; Gentili, Michele; [et al.]
Área de conocimientoIngeniería EléctricaIngeniería QuímicaQuímica-Física
PatrocinadoresThe 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/conUniversidad Politécnica de Cartagena; Universidad de Murcia; Sapienza University Rome
Fecha de publicación2019-10-16
Cita bibliográficaRodríguez-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/s19204482
Revisión por paresSi
Palabras claveContinuous glucose monitoring
Univariate time series
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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