{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:33:25Z","timestamp":1791909205096,"version":"4.3.4"},"reference-count":41,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,16]],"date-time":"2019-10-16T00:00:00Z","timestamp":1571184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PERSEIDES (ref. TIN2017-86885-R)","award":["PERSEIDES (ref. TIN2017-86885-R)"],"award-info":[{"award-number":["PERSEIDES (ref. TIN2017-86885-R)"]}]},{"name":"CHIST-ERA (ref. PCIN-2016-010)","award":["CHIST-ERA (ref. PCIN-2016-010)"],"award-info":[{"award-number":["CHIST-ERA (ref. PCIN-2016-010)"]}]},{"name":"MINECO grant BES-2015-071956","award":["MINECO grant BES-2015-071956"],"award-info":[{"award-number":["MINECO grant BES-2015-071956"]}]},{"name":"H2020-ENTROPY-649849 EU Project","award":["H2020-ENTROPY-649849 EU Project"],"award-info":[{"award-number":["H2020-ENTROPY-649849 EU Project"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Machine learning techniques combined with wearable electronics can deliver accurate short-term blood glucose level prediction models. These models can learn personalized glucose\u2013insulin 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.<\/jats:p>","DOI":"10.3390\/s19204482","type":"journal-article","created":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T04:46:06Z","timestamp":1571287566000},"page":"4482","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Utility of Big Data in Predicting Short-Term Blood Glucose Levels in Type 1 Diabetes Mellitus Through Machine Learning Techniques"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0118-3406","authenticated-orcid":false,"given":"Ignacio","family":"Rodr\u00edguez-Rodr\u00edguez","sequence":"first","affiliation":[{"name":"Facultad de Inform\u00e1tica, Departamento de Ingenier\u00eda de la Informaci\u00f3n y las Comunicaciones, Universidad de Murcia, 30100 Murcia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8955-9270","authenticated-orcid":false,"given":"Ioannis","family":"Chatzigiannakis","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Informatica Automatica e Gestionale \u2018Antonio Ruberti\u2019, Sapienza Universit\u00e0 di Roma, 00185 Roma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3298-6439","authenticated-orcid":false,"given":"Jos\u00e9-V\u00edctor","family":"Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Departamento de Tecnolog\u00edas de la Informaci\u00f3n y las Comunicaciones, Universidad Polit\u00e9cnica de Cartagena; 30202 Cartagena, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marianna","family":"Maranghi","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Informatica Automatica e Gestionale \u2018Antonio Ruberti\u2019, Sapienza Universit\u00e0 di Roma, 00185 Roma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Gentili","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Informatica Automatica e Gestionale \u2018Antonio Ruberti\u2019, Sapienza Universit\u00e0 di Roma, 00185 Roma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0130-2364","authenticated-orcid":false,"given":"Miguel-\u00c1ngel","family":"Zamora-Izquierdo","sequence":"additional","affiliation":[{"name":"Facultad de Inform\u00e1tica, Departamento de Ingenier\u00eda de la Informaci\u00f3n y las Comunicaciones, Universidad de Murcia, 30100 Murcia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.2337\/diaclin.25.1.25","article-title":"Diabetes: Magnitude and mechanisms","volume":"25","author":"Fowler","year":"2007","journal-title":"Clinical Diabetes"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2254","DOI":"10.1001\/jama.289.17.2254","article-title":"Outpatient insulin therapy in type 1 and type 2 diabetes mellitus: Scientific review","volume":"289","author":"DeWitt","year":"2003","journal-title":"JAMA"},{"key":"ref_3","unstructured":"Davidson, M.B., and Davidson, M.B. 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