{"id":24038,"date":"2022-02-14T08:45:25","date_gmt":"2022-02-14T14:45:25","guid":{"rendered":"https:\/\/saluddigital.com\/?p=24038"},"modified":"2025-10-21T10:20:22","modified_gmt":"2025-10-21T16:20:22","slug":"el-rol-del-aprendizaje-automatico-en-la-toma-de-decisiones-sobre-covid-19","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/el-rol-del-aprendizaje-automatico-en-la-toma-de-decisiones-sobre-covid-19\/","title":{"rendered":"The role of machine learning in decision-making on COVID-19"},"content":{"rendered":"
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

A recent study by the Indiana University Regenstrief Institute showed the application of machine learning models to predict the probability of hospitalization for COVID-19 in infected patients.<\/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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Machine learning has been an important medical resource to support decision-making during the pandemic in the United States. Researchers at the Regenstrief Institute published a study detailing how machine learning models, trained on clinical data from across the state of Indiana, were able to predict the likelihood of hospitalization for people who contracted COVID-19.<\/p>

"What we thought to do was take advantage of this urgent need to create machine learning solutions that could help health care decision makers make general decisions about the level of health of the population they serve," explained Dr. Suranga Kasturi, first author of the study.<\/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

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\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
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

The objective of the study was to develop technology that favors the effective use of available resources. "Whether it's the state, or the county, or some other region, you can effectively estimate for the next week or six weeks how many health care resources you're going to need," Kasturi said.<\/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

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

The clinical data used for the study are part of the Indiana Network for Patient Care<\/em> (INPC), which contains more than 14 billion pieces of clinical patient information. For the study they collected information from 96 thousand 26 patients.<\/p>

\u201cIn the context of this study, having machine learning solutions help us harness vast amounts of data to help inform broad decisions that affect population and public health. Things that we just couldn't do if there wasn't an AI-powered system to work with such large amounts of data."<\/p>

In this way, the developed models achieved a great performance in their predictions, however, when specifying the variables in subpopulations of the state, there were some disparities. As explained by Dr. Kasturi, the models worked well, however, there is a lot of room for improvement to enhance the performance of the algorithms.<\/p>

\u201cThe next step would be to address those disparities, ensuring that the model provides fair and equitable care. Once that happens, we'll be able to have broader conversations in terms of how it can be integrated into existing public health workflows."<\/p>

The study is available at the following link: https:\/\/www.jmir.org\/2021\/11\/e31337<\/a> \u00a0\u00a0<\/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

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t
\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

HEALTH IT ANALYTICS<\/strong><\/p>

https:\/\/healthitanalytics.com\/features\/how-machine-learning-can-guide-covid-19-decision-making<\/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 estudio reciente del Regenstrief Institute de la Universidad de Indiana, mostr\u00f3 la aplicaci\u00f3n de modelos de aprendizaje autom\u00e1tica para predecir probabilidad de hospitalizaci\u00f3n por COVID-19 en pacientes contagiados. El aprendizaje autom\u00e1tico ha sido un importante recurso m\u00e9dico para favorecer la toma de decisiones durante la pandemia en Estados Unidos. Investigadores del Regenstrief Institute, publicaron un estudio que detall\u00f3 c\u00f3mo los modelos de aprendizaje autom\u00e1tico, entrenados con datos cl\u00ednicos de todo el estado de Indiana, lograron predecir la probabilidad de hospitalizaci\u00f3n de personas que se contagiaron de COVID-19. \u201cLo que pensamos hacer fue aprovechar esta necesidad urgente de crear soluciones de aprendizaje autom\u00e1tico que pudieran ayudar a los tomadores de decisiones de atenci\u00f3n m\u00e9dica a tomar decisiones generales sobre el nivel de salud de la poblaci\u00f3n a las que atienden\u201d, explic\u00f3 el Dr. Suranga Kasturi, primer autor del estudio. El objetivo del estudio fue desarrollar tecnolog\u00eda que favoreciera la utilizaci\u00f3n efectiva de los recursos disponibles. \u201cYa sea el estado, el condado o alguna otra regi\u00f3n, puede estimar efectivamente para la pr\u00f3xima semana o seis semanas cu\u00e1ntos recursos de atenci\u00f3n m\u00e9dica necesitar\u00e1\u201d, dijo Kasturi. Los datos cl\u00ednicos utilizados para el estudio, forman parte del Indiana Network for Patient Care (INPC), la cual contiene m\u00e1s de 14 mil millones de datos de informaci\u00f3n cl\u00ednica de pacientes. Para el estudio recolectaron informaci\u00f3n de 96 mil 26 pacientes. \u201cEn el contexto de este estudio, tener soluciones de aprendizaje autom\u00e1tico nos ayuda a aprovechar grandes cantidades de datos para ayudar a informar decisiones amplias que afectan la salud p\u00fablica y de la poblaci\u00f3n. Cosas que simplemente no podr\u00edamos hacer si no hubiera un sistema impulsado por IA para trabajar con cantidades tan grandes de datos\u201d, explic\u00f3 el especialista. De esta forma los modelos desarrollados alcanzaron un gran desempe\u00f1o en sus predicciones, sin embargo, al especificar las variables en subpoblaciones del estado existieron algunas disparidades. Seg\u00fan explic\u00f3 el Dr. Kasturi, los modelos funcionaron bien, sin embargo, hay un gran margen de mejora para potencializar el rendimiento de los algoritmos. \u201cEl pr\u00f3ximo paso ser\u00eda abordar esas disparidades, asegurando que el modelo brinde una atenci\u00f3n justa y equitativa. Una vez que eso suceda, podremos tener conversaciones m\u00e1s amplias en t\u00e9rminos de c\u00f3mo se puede integrar en los flujos de trabajo de salud p\u00fablica existentes\u201d, concluy\u00f3. El estudio est\u00e1 disponible en el siguiente enlace: https:\/\/www.jmir.org\/2021\/11\/e31337    BIBLIOGRAF\u00cdA HEALTH IT ANALYTICS https:\/\/healthitanalytics.com\/features\/how-machine-learning-can-guide-covid-19-decision-making<\/p>","protected":false},"author":1,"featured_media":24040,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,160,1418],"tags":[145],"class_list":["post-24038","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\/24038","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=24038"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/24038\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/24040"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=24038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=24038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=24038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}