{"id":26382,"date":"2022-05-19T09:20:49","date_gmt":"2022-05-19T14:20:49","guid":{"rendered":"https:\/\/saluddigital.com\/?p=26382"},"modified":"2025-10-20T11:56:30","modified_gmt":"2025-10-20T17:56:30","slug":"utilizan-aprendizaje-automatico-para-la-identificacion-de-covid-de-larga-duracion","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/utilizan-aprendizaje-automatico-para-la-identificacion-de-covid-de-larga-duracion\/","title":{"rendered":"They use machine learning for the identification of long-lived COVID"},"content":{"rendered":"
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A study published in The Lancet showed a method for identifying patients with long-term COVID-19 through machine learning and data from the National COVID Cohort Collaborative's (N3C).<\/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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Prolonged or long-term COVID is characterized by various symptoms such as shortness of breath, fatigue, fever, headache, and other neurological problems. These symptoms can last for several months after the diagnosis of COVID-19. After having symptoms similar to a normal COVID-19 infection, it is necessary to create methods to make its diagnosis more effective and timely.<\/p>

Medical researchers at the US National Institutes of Health (NIH) have developed a method for identifying patients with prolonged COVID-19, with the aim of developing faster diagnosis and treatment. They used data from the N3C, an electronic repository of electronic medical records.<\/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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Through the N3C data, the researchers developed a machine learning model, for the potential detection of patients with prolonged COVID. The patient base included more than 1.7 million people over the age of 18, with records of having attended emergency services for COVID-19, or having tested positive for the infection.<\/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

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They also examined the treatment and medication used in more than 97,000 adults who suffered from COVID-19. And in the same way they analyzed data from 597 patients who suffered from long-term COVID.<\/p>

Thanks to machine learning, the researchers were able to train the models to sift through massive amounts of data. The models looked for patterns in the data mentioned above and were able to identify the main characteristics of patients diagnosed with long-standing COVID.<\/p>

The study was able to identify characteristics of the disease, patient visits to the doctor, symptoms other than COVID-19. \u201cOnce you can determine who has prolonged COVID in a large database of people, you can start asking questions about those people,\u201d said Dr. Josh Fessel, clinical advisor to the NIH RECOVER Initiative.<\/p>

On the other hand, Dr. Melissa Haendel, one of the authors of the study, explained that for the identification of patients with prolonged COVID it is important to carry out clinical trials. \u201cThe models can learn from a wider variety of patients and become more accurate. We look forward to using our long COVID patient classifier for clinical trial recruitment.\u201d<\/p>

\u00a0Click on the following link to view the full study:<\/p>

https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(22)00048-6\/fulltext<\/a><\/p>

Lorem fistrum for the glory of my mother esse jarl aliqua take me to the sircoo. From the ullamco prairie what do you say is the very bad thing.<\/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 LANCET <\/strong><\/p>

https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(22)00048-6\/fulltext<\/a><\/p>

SCIENCE DAILY<\/strong><\/p>

https:\/\/www.sciencedaily.com\/releases\/2022\/05\/220516213143.htm<\/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 The Lancet, mostr\u00f3 un m\u00e9todo para la identificaci\u00f3n de pacientes con COVID-19 de larga duraci\u00f3n a trav\u00e9s de aprendizaje autom\u00e1tico y datos del National COVID Cohort Collaborative’s (N3C). COVID prolongado o de larga duraci\u00f3n, se caracteriza por incluir diversos s\u00edntomas como dificultad para respirar, fatiga, fiebre, dolor de cabeza, y otros problemas neurol\u00f3gicos.  Estos s\u00edntomas pueden durar varios meses despu\u00e9s del diagn\u00f3stico de COVID-19. Tras contar con s\u00edntomas similares a una infecci\u00f3n normal por COVID-19, es necesario crear m\u00e9todos para que su diagn\u00f3stico sea m\u00e1s eficaz y oportuna. Investigadores m\u00e9dicos de los Institutos Nacionales de Salud de Estados Unidos (NIH), desarrollaron un m\u00e9todo para la identificaci\u00f3n de pacientes con COVID-19 prolongado, con el objetivo de desarrollar tratamientos y diagn\u00f3sticos m\u00e1s r\u00e1pidos. Para ello utilizaron datos del N3C, un repositorio electr\u00f3nico de registros m\u00e9dicos electr\u00f3nicos. A trav\u00e9s de los datos el N3C, los investigadores desarrollaron un modelo de aprendizaje autom\u00e1tico, para la detecci\u00f3n potencial de pacientes con COVID prolongado. La base de pacientes incluy\u00f3 m\u00e1s de 1.7 millones de personas mayores de 18 a\u00f1os, con registros de haber asistido a servicios de emergencia por COVID-19, o de haber obtenido una prueba positiva a la infecci\u00f3n. Asimismo, examinaron el tratamiento y la medicaci\u00f3n utilizada en m\u00e1s de 97 mil adultos que padecieron COVID-19. Y de igual forma analizaron datos de 597 pacientes que padecieron COVID de larga duraci\u00f3n. Gracias al aprendizaje autom\u00e1tico, los investigadores lograron entrenar los modelos para filtrar cantidades masivas de datos. Los modelos buscaron patrones en los datos mencionados anteriormente y pudieron identificar las caracter\u00edsticas principales de pacientes con diagn\u00f3stico de COVID de larga duraci\u00f3n. El estudio logr\u00f3 identificar caracter\u00edsticas de la enfermedad, visitas al m\u00e9dico de los pacientes, s\u00edntomas distintos a COVID-19. \u201cUna vez que pueda determinar qui\u00e9n tiene COVID prolongado en una gran base de datos de personas, puede comenzar a hacer preguntas sobre esas personas\u201d, dijo el Dr. Josh Fessel, asesor cl\u00ednico de RECOVER Initiative de los NIH. Por otra parte, la Dra. Melissa Haendel, una de las autoras del estudio, explic\u00f3 que para la identificaci\u00f3n de pacientes con COVID prolongado es importante para realizar ensayos cl\u00ednicos. \u201cLos modelos pueden aprender de una mayor variedad de pacientes y volverse m\u00e1s precisos. Esperamos poder usar nuestro clasificador largo de pacientes con COVID para el reclutamiento de ensayos cl\u00ednicos\u201d.  Ingresa al siguiente enlace para consultar el estudio completo: https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(22)00048-6\/fulltext Lorem fistrum por la gloria de mi madre esse jarl aliqua llevame al sircoo. De la pradera ullamco qu\u00e9 dise usteer est\u00e1 la cosa muy malar. BIBLIOGRAF\u00cdA THE LANCET https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(22)00048-6\/fulltext SCIENCE DAILY https:\/\/www.sciencedaily.com\/releases\/2022\/05\/220516213143.htm<\/p>","protected":false},"author":1,"featured_media":26384,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,3400,160,3396],"tags":[145],"class_list":["post-26382","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-diagnostico","category-noticias","category-uso-de-plataformas-digitales","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/26382","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=26382"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/26382\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/26384"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=26382"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=26382"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=26382"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}