{"id":19204,"date":"2021-08-04T09:23:44","date_gmt":"2021-08-04T14:23:44","guid":{"rendered":"https:\/\/saluddigital.com\/?p=19204"},"modified":"2025-10-21T12:21:33","modified_gmt":"2025-10-21T18:21:33","slug":"investigacion-muestra-modelo-de-prediccion-de-complicacion-por-covid-19-a-traves-de-conjunto-de-datos-de-expedientes-clinicos-electronicos","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/investigacion-muestra-modelo-de-prediccion-de-complicacion-por-covid-19-a-traves-de-conjunto-de-datos-de-expedientes-clinicos-electronicos\/","title":{"rendered":"Research shows COVID-19 complication prediction model through electronic medical record dataset"},"content":{"rendered":"
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The prediction of critical condition after the diagnosis of COVID-19, was possible in the United States, thanks to the development of models with data from electronic medical records (EHR).<\/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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The research was published in the journal Nature under the title: \u201cPredicting critical status after COVID-19 diagnosis: model development using a large US electronic medical record dataset.\u201d The article presents a prognostic model of the critical state of COVID patients, 28 days after the diagnosis of the disease.<\/p>

The availability of current data allowed techniques such as machine learning to be used during the pandemic, as exemplified in this study. In addition, in combination with the ECE and the large network of hospitals in the United States, it was possible to obtain relevant results.<\/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 model developed for the research included demographic data, comorbidities, symptoms, and hospitalization. The data included information on 15,753 patients, of which 2,050 were in critical condition or even lost their lives.<\/p>

The interpretability analysis of the data confirmed the main risk factors efficiently, which were the following: Advanced age, higher body mass index, male sex, diabetes and cardiovascular disease.<\/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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ECEs can be a powerful data source for creating evidence based on real-world data, especially when combined with a platform that facilitates structured data mining.<\/p>

\u201cThe results of this work demonstrate that it is possible to develop an explainable machine learning model based on patient-level EHR data to predict, at the time of COVID-19 diagnosis, whether individual patients will progress to critical status in the following weeks. 4 weeks\u201d, explains the study on the results.<\/p>

The study recognizes the biases that can be found in this type of model, since it mainly considers metropolitan areas, so, to be considered in clinical implementation, it must be validated with other data sets.<\/p>

Check the full study at the following link: https:\/\/www.nature.com\/articles\/s41746-021-00482-9<\/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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\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\/s41746-021-00482-9<\/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":"

La predicci\u00f3n de estado cr\u00edtico despu\u00e9s del diagn\u00f3stico de COVID-19, fue posible en Estados Unidos, gracias al desarrollo de modelos con datos de registros m\u00e9dicos electr\u00f3nicos (ECE). Fue publicada en la revista Nature la investigaci\u00f3n con el t\u00edtulo: “Predicci\u00f3n del estado cr\u00edtico despu\u00e9s del diagn\u00f3stico de COVID-19: desarrollo de modelos utilizando un gran conjunto de datos de registros m\u00e9dicos electr\u00f3nicos de EE.UU.”. El art\u00edculo presenta un modelo de pron\u00f3stico del estado cr\u00edtico de pacientes COVID, 28 d\u00edas despu\u00e9s del diagn\u00f3stico de la enfermedad. La disponibilidad de datos actuales permiti\u00f3 que durante la pandemia se utilizaran t\u00e9cnicas como el aprendizaje autom\u00e1tico, tal y como se ejemplifica en este estudio. Adem\u00e1s, en combinaci\u00f3n con los ECE y la gran red de hospitales en Estados Unidos, fue posible obtener resultados relevantes. El modelo desarrollado para la investigaci\u00f3n incluy\u00f3 datos demogr\u00e1ficos, comorbilidades, s\u00edntomas y hospitalizaci\u00f3n. Los datos incluyeron informaci\u00f3n de 15 mil 753 pacientes, de los cuales 2050 entraron en estado cr\u00edtico o incluso perdieron la vida. El an\u00e1lisis de interpretabilidad de los datos, confirm\u00f3 los principales factores de riesgo eficientemente, los cuales fueron los siguientes: Edad avanzada, mayor \u00edndice de masa corporal, sexo masculino, diabetes y enfermedad cardiovascular. \u201cLos ECE pueden ser una fuente de datos poderosa para crear evidencia basada en datos del mundo real, especialmente cuando se combinan con una plataforma que facilita la extracci\u00f3n estructurada de datos\u201d, explica el estudio. \u201cLos resultados de este trabajo demuestran que es posible desarrollar un modelo de aprendizaje autom\u00e1tico explicable basado en datos de HCE a nivel de paciente para predecir en el momento del diagn\u00f3stico de COVID-19, si los pacientes individuales progresar\u00e1n a un estado cr\u00edtico en las siguientes 4 semanas\u201d, explica el estudio sobre los resultados. El estudio reconoce los sesgos que pueden encontrarse en este tipo de modelos, ya que considera principalmente \u00e1reas metropolitanas, por lo que, para ser considerado en la implementaci\u00f3n cl\u00ednica, debe validarse con otros conjuntos de datos. Consulta el estudio completo en el siguiente enlace: https:\/\/www.nature.com\/articles\/s41746-021-00482-9 BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41746-021-00482-9<\/p>","protected":false},"author":1,"featured_media":19206,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,160,1418],"tags":[145],"class_list":["post-19204","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\/19204","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=19204"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/19204\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/19206"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=19204"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=19204"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=19204"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}