{"id":20794,"date":"2021-08-30T00:03:57","date_gmt":"2021-08-30T05:03:57","guid":{"rendered":"https:\/\/saluddigital.com\/?p=20794"},"modified":"2025-10-21T12:06:46","modified_gmt":"2025-10-21T18:06:46","slug":"nuevo-modelo-de-aprendizaje-automatico-para-la-prediccion-de-niveles-de-glucosa-sin-necesidad-de-pinchar-los-dedos","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/nuevo-modelo-de-aprendizaje-automatico-para-la-prediccion-de-niveles-de-glucosa-sin-necesidad-de-pinchar-los-dedos\/","title":{"rendered":"New machine learning model for predicting glucose levels without the need for finger pricks"},"content":{"rendered":"
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An article published in Nature details a novel model for predicting blood glucose levels without the need for devices or needle sticks.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Type 2 diabetes is one of the biggest public health problems worldwide. Many treatments are aimed at reducing blood sugar levels, however, the most important thing is prevention and promoting healthy lifestyles, such as a good diet and physical exercise.<\/p>

Research<\/a> show that patients show greater motivation to improve decisions about their diet and exercise, when they have accurate and continuous data on their glucose levels. This is one of the main barriers, since continuous glucose monitoring is performed only through digital punctures, that is, an invasive technique.<\/p>

Researchers from the Department of Biomedical Engineering at Duke University have developed a machine learning model for the prediction of interstitial glucose levels through non-invasive measurements. Through the glucose fluctuations reflected by the heart rate of the patients, they developed this model that combines 69 inputs for the prediction of glucose levels in the 16 study participants (patients with diagnosed prediabetes or with blood glucose levels normal to high).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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"Model inputs included demographic\/historical data such as biological sex, food log (recorded by study participant), and last HgbA1c measurement, as well as biomarkers of stress, activity, and circadian rhythm," explain Leia Wedlund and Joseph Kvedar of Harvard Medical School in his editorial published in Nature.<\/p>

The algorithm was trained using a continuous through monitor, which provided true interstitial glucose levels. The model used this data as a reference point to determine high glucose levels. Using data such as heart rate or daily diet, the model was able to predict the exact levels of interstitial glucose of the participants, this without using invasive techniques and registering an accuracy of 87%.<\/p>

The original study \u201cEngineering Digital Interstitial Glucose Biomarkers from Non-Invasive Smart Watches\u201d by Brinnae Bent, et al. you can find it at the following link: https:\/\/www.nature.com\/articles\/s41746-021-00465-w<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t BIBLIOGRAPHY<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

NATURE<\/strong><\/p>

https:\/\/www.nature.com\/articles\/s41746-021-00501-9<\/a><\/p>

https:\/\/www.nature.com\/articles\/s41746-021-00465-w<\/a><\/p>

STATISA<\/strong><\/p>

https:\/\/www.statista.com\/statistics\/241802\/number-of-diabetics-worldwide-by-region\/<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Un art\u00edculo publicado en Nature, detalla un novedoso modelo para la predicci\u00f3n de niveles de glucosa en sangre sin necesidad de dispositivos ni pinchazos. La diabetes tipo 2 es uno de los problemas m\u00e1s grandes de salud p\u00fablica a nivel mundial. Muchos tratamientos est\u00e1n destinados a la reducci\u00f3n del nivel de az\u00facar en sangre, sin embargo, lo m\u00e1s importante es la prevenci\u00f3n y fomentar estilos de vida saludables, como una buena dieta y ejercicio f\u00edsico. Investigaciones muestran que los pacientes muestran mayor motivaci\u00f3n sobre mejorar las decisiones sobre su dieta y ejercicio, cuando cuentan con datos precisos y continuos sobre sus niveles de glucosa. Esta es una de las principales barreras, ya que la monitorizaci\u00f3n continua de glucosa se realiza solamente mediante pinchazos digitales, es decir una t\u00e9cnica invasiva. Investigadores del Departamento de Ingenier\u00eda Biom\u00e9dica de la Universidad de Duke, han desarrollado un modelo de aprendizaje autom\u00e1tico para la predicci\u00f3n de niveles de glucosa intersticial mediante mediciones no invasivas. A trav\u00e9s de las fluctuaciones de la glucosa reflejadas por la frecuencia cardiaca de los pacientes, desarrollaron este modelo que combina 69 entradas para la predicci\u00f3n de los niveles de glucosa en los 16 participantes del estudio (pacientes con prediabetes diagnosticada o con niveles de glucosa en sangre normales a altos). \u201cLas entradas del modelo incluyeron datos demogr\u00e1ficos \/ hist\u00f3ricos como el sexo biol\u00f3gico, el registro de alimentos (registrado por el participante del estudio) y la \u00faltima medici\u00f3n de HgbA1c, as\u00ed como biomarcadores de estr\u00e9s, actividad y ritmo circadiano\u201d, explican Leia Wedlund y Joseph Kvedar de escuela de medicina de Harvard en su editorial publicada e Nature. El algoritmo fue entrenado utilizando un monitor continuo trav\u00e9s, que proporcion\u00f3 los verdaderos niveles de glucosa intersticial. El modelo utilizo este dato como punto de referencia para determinar los niveles altos de glucosa. Mediante datos como la frecuencia card\u00edaca o la dieta diaria el modelo logr\u00f3 predecir los niveles exactos de glucosa intersticial de los participantes, esto sin utilizar t\u00e9cnicas invasivas y registrar una precisi\u00f3n de 87%. El estudio original \u201cIngenier\u00eda de biomarcadores digitales de glucosa intersticial a partir de relojes inteligentes no invasivos\u201d de Brinnae Bent, et al. puedes encontrarlo en el siguiente enlace: https:\/\/www.nature.com\/articles\/s41746-021-00465-w BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41746-021-00501-9 https:\/\/www.nature.com\/articles\/s41746-021-00465-w STATISA https:\/\/www.statista.com\/statistics\/241802\/number-of-diabetics-worldwide-by-region\/<\/p>","protected":false},"author":1,"featured_media":20795,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,160,1418],"tags":[145],"class_list":["post-20794","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-big-data","category-inteligencia-artificial-y-ciencia","category-noticias","category-resena-de-publicaciones-cientificas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/20794","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=20794"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/20794\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/20795"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=20794"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=20794"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=20794"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}