{"id":15478,"date":"2021-05-28T10:20:06","date_gmt":"2021-05-28T15:20:06","guid":{"rendered":"https:\/\/saluddigital.com\/?p=15478"},"modified":"2025-10-21T12:39:43","modified_gmt":"2025-10-21T18:39:43","slug":"sistema-de-inteligencia-artificial-para-predecir-el-avance-de-covid-19-en-pacientes-de-urgencias","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/sistema-de-inteligencia-artificial-para-predecir-el-avance-de-covid-19-en-pacientes-de-urgencias\/","title":{"rendered":"Artificial Intelligence system to predict COVID-19 progression in emergency department patients"},"content":{"rendered":"
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During the pandemic, fast action and proper triage of patients in the emergency department has been key to preventing complications of COVID-19.<\/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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Scientists in the United States proposed and applied a data-driven system \" for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables,\" as explained in their paper in Nature.<\/p>

The prognostic system through Artificial Intelligence (AI), was trained with data from 3,661 patients and achieved an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) in predicting deterioration within 96 hours.<\/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 a neural network, informative areas are extracted from X-ray chest images to help specialists interpret the predictions. It also reduces the working time of radiologists, and takes advantage of this type of specialists for other tasks within the hospital.<\/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 AI system was implemented in its preliminary version at New York University's Langone Health during the first wave of contagions. During its application, it made accurate predictions in real time.<\/p>

\u201cOur findings show the promise of data-driven AI systems in predicting the risk of deterioration for COVID-19 patients, and highlights the importance of designing multi-modal AI systems capable of processing different types of data,\u201d the authors conclude.<\/p>

The datasets used are available on ImageNet. And the code for the study models along with their trainings are available on GitHub. See the full article here: https:\/\/www.nature.com\/articles\/s41746-021-00453-0#data-availability<\/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\/s41746-021-00453-0<\/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":"

Durante la pandemia el accionar r\u00e1pido y realizar un triage adecuado de los pacientes en el departamento de urgencias ha sido clave, para prevenir complicaciones de COVID-19. Cient\u00edficos en Estados Unidos propusieron y aplicaron un sistema basado en datos \u201cpara la predicci\u00f3n autom\u00e1tica del riesgo de deterioro utilizando una red neuronal profunda que aprende de las im\u00e1genes de rayos X de t\u00f3rax y un modelo de aumento de gradiente que aprende de las variables cl\u00ednicas de rutina\u201d, seg\u00fan explican en su art\u00edculo en Nature. El sistema de pron\u00f3stico a trav\u00e9s de Inteligencia Artificial (IA), fue entrenado con datos de 3,661 pacientes y alcanz\u00f3 un \u00e1rea bajo la curva caracter\u00edstica operativa del receptor (AUC) de 0,786 (IC del 95%: 0,745\u20130,830) al predecir el deterioro en 96 horas. A trav\u00e9s de una red neuronal se extraen \u00e1reas informativas de im\u00e1genes de t\u00f3rax obtenidas por rayos X y as\u00ed ayudar a los especialistas a interpretar las predicciones. Asimismo, reduce el tiempo de trabajo de radi\u00f3logos, y aprovechar a este tipo de especialistas para otras tareas dentro del hospital. El sistema de IA, fue implementado en su versi\u00f3n preliminar en Langone Health de la Universidad de Nueva York, durante la primera ola de contagios. Durante su aplicaci\u00f3n realizo predicciones precisas en tiempo real. “Nuestros hallazgos demuestran el potencial del sistema propuesto para ayudar a los m\u00e9dicos de primera l\u00ednea en el triaje de pacientes con COVID-19”, concluyen los autores. Los conjuntos de datos utilizados est\u00e1n disponibles en ImageNet. Y el c\u00f3digo de los modelos del estudio junto con sus entrenamientos est\u00e1n disponibles en GitHub. Consulta el art\u00edculo completo aqu\u00ed: https:\/\/www.nature.com\/articles\/s41746-021-00453-0#data-availability BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41746-021-00453-0<\/p>","protected":false},"author":1,"featured_media":15482,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,1418],"tags":[145],"class_list":["post-15478","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-big-data","category-inteligencia-artificial-y-ciencia","category-resena-de-publicaciones-cientificas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/15478","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=15478"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/15478\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/15482"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=15478"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=15478"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=15478"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}