{"id":29994,"date":"2022-07-28T10:14:08","date_gmt":"2022-07-28T15:14:08","guid":{"rendered":"https:\/\/saluddigital.com\/?p=29994"},"modified":"2025-10-20T11:30:30","modified_gmt":"2025-10-20T17:30:30","slug":"estudio-utiliza-aprendizaje-profundo-para-predecir-la-esperanza-de-vida-a-traves-de-estudios-del-sueno","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/estudio-utiliza-aprendizaje-profundo-para-predecir-la-esperanza-de-vida-a-traves-de-estudios-del-sueno\/","title":{"rendered":"Study uses deep learning to predict life expectancy through sleep studies"},"content":{"rendered":"
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Study published in npj Digital Medicine shows the estimation of age from sleep studies that use deep learning and manages to predict life expectancy.<\/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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Sleep disturbances increase with age and are predictors of mortality, according to the authors of the study. The gold standard diagnostic test for evaluating patient sleep is overnight polysomnography (PSG). This test is made up of physiological signals such as electroencephalogram, electrocardiogram, electrooculogram, chin and leg electromyogram, respiratory effort, and airflow. All of this data is recorded overnight while the patient sleeps and remains mostly immobile.<\/p>

Although the main reasons for conducting clinical sleep studies are related to simple metrics such as sleep latency, proportion of time in sleep stages, sleep apnea, recent studies have shown deep learning methods to help efficiently and objectively to PSG analyses.<\/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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The new algorithms offer additional information such as higher resolution sleep stages and probabilistic measures. For example, taking into account that aging affects sleep architecture, it is possible to model age as an indicator of mortality risk using deep learning models.<\/p>

The study presented by American and Danish authors shows deep neural networks that estimate age and mortality risk through PSG. They modeled 250 PSGs and tested them on 10,699 PSGs of men and women in seven different cohorts between the ages of 20 and 90. The estimation of ages had a mean absolute error of 5.8 \u00b1 1.6 years and the basic measures of sleep scores had an error of 14.9 \u00b1 6.29 years.<\/p>

\u201cA strength of this study is the inclusion of multiple cohorts, which is likely to increase the generalizability of our models. However, this is also a limitation, since measuring sleep with common instrumentation and in a more controlled environment could have better predictive power by reducing technical noise, first-night effect, variation in recording equipment , the location of the electrodes, the ambient temperature and external noise, etc.\u201d, explains the study.<\/p>

Learn more about this study: https:\/\/www.nature.com\/articles\/s41746-022-00630-9<\/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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NATURE<\/strong><\/p>

https:\/\/www.nature.com\/articles\/s41746-022-00630-9<\/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":"

Estudio publicado en npj Digital Medicine muestra la estimaci\u00f3n de la edad a partir de estudios del sue\u00f1o que utilizan aprendizaje profundo y logra predecir la esperanza de vida. Las alteraciones del sue\u00f1o aumentan con la edad y son predictores de mortalidad, seg\u00fan explican los autores del estudio. La prueba diagn\u00f3stica de est\u00e1ndar de oro para evaluar el sue\u00f1o de pacientes es la polisomnograf\u00eda nocturna (PSG, en ingl\u00e9s). Esta prueba se compone de se\u00f1ales fisiol\u00f3gicas como electroencefalograma, electrocardiograma, electrooculograma, electromiograma de ment\u00f3n y pierna, esfuerzo respiratorios y flujo de aire. Todos estos datos se registran durante la noche mientras el paciente duerme y permanece mayormente inm\u00f3vil. A pesar de que las principales razones para realizar estudios cl\u00ednicos de sue\u00f1o se relacionan con m\u00e9tricas simples como latencia de sue\u00f1o, proporci\u00f3n de tiempo en etapas de sue\u00f1o, apnea de sue\u00f1o, estudios recientes han mostrado m\u00e9todos de aprendizaje profundo que ayudan de manera eficiente y objetiva a los an\u00e1lisis de PSG. Los nuevos algoritmos ofrecen informaci\u00f3n adicional como etapas de sue\u00f1o de mayor resoluci\u00f3n y medidas probabil\u00edsticas. Por ejemplo, tomando en cuenta que el envejecimiento afecta la arquitectura del sue\u00f1o, es posible modelar la edad como un indicador de riesgo de mortalidad utilizando modelos de aprendizaje profundo. El estudio presentado por autores estadounidenses y daneses, muestra redes neuronales profundas que estiman la edad y el riesgo de mortalidad a trav\u00e9s de PSG. Para ello modelaron 250 PSG y se probaron en 10 mil 699 PSG de hombres y mujeres en siete cohortes diferentes de entre 20 y 90 a\u00f1os. La estimaci\u00f3n de las edades obtuvo un error absoluto medio de 5,8 \u00b1 1,6 a\u00f1os y las medidas b\u00e1sicas de puntuaci\u00f3n del sue\u00f1o tuvieron un error de 14,9 \u00b1 6,29 a\u00f1os. \u201cUna fortaleza de este estudio es la inclusi\u00f3n de m\u00faltiples cohortes, lo que probablemente aumente la generalizaci\u00f3n de nuestros modelos. Sin embargo, esto tambi\u00e9n es una limitaci\u00f3n, ya que medir el sue\u00f1o con una instrumentaci\u00f3n com\u00fan y en un entorno m\u00e1s controlado podr\u00eda tener un mejor poder predictivo al reducir el ruido t\u00e9cnico, el efecto de la primera noche, la variaci\u00f3n en el equipo de registro, la ubicaci\u00f3n de los electrodos, la temperatura ambiente y el ruido externo, etc.\u201d, explica el estudio. Conoce m\u00e1s sobre este estudio: https:\/\/www.nature.com\/articles\/s41746-022-00630-9 BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41746-022-00630-9<\/p>","protected":false},"author":1,"featured_media":29996,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,160],"tags":[145],"class_list":["post-29994","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-big-data","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/29994","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=29994"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/29994\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/29996"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=29994"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=29994"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=29994"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}