{"id":25096,"date":"2022-05-05T07:27:49","date_gmt":"2022-05-05T12:27:49","guid":{"rendered":"https:\/\/saluddigital.com\/?p=25096"},"modified":"2025-10-20T11:59:12","modified_gmt":"2025-10-20T17:59:12","slug":"modelos-de-aprendizaje-automatico-muestran-signos-vitales-asociados-con-casos-severos-de-covid-19","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/modelos-de-aprendizaje-automatico-muestran-signos-vitales-asociados-con-casos-severos-de-covid-19\/","title":{"rendered":"Machine learning models show vital signs associated with severe cases of COVID-19"},"content":{"rendered":"
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New research showed machine learning-based risk models indicating vital signs, lab test results, and age as factors associated with severe cases 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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Magazine Scientific Reports<\/em> of Nature<\/em>, published the article \u201cRisk factors for severe COVID-19 differ by age in hospitalized adults,\u201d which used machine learning-based risk models to find that vital signs, lab test results, and age , were correlated with the level of complication of the COVID-19 infection.<\/p>

The study analyzed data from 6,906 adults who were hospitalized for COVID-19, in five US states. In this sense, they developed risk models to predict the use of mechanical ventilation or the death of the patient between the first day and the 56th day of hospitalization.<\/p>

In total, five machine learning models were trained for the study for a population of all ages and for two other subpopulations of different ages, 18 years or older and older than 50 years. Furthermore, risk models were trained using readily available clinical data.<\/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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In this way, the reference data available one hour after the admission of adult patients to the hospital, as well as the first positive test for COVID-19, were used, which were vital for the prediction of critical illnesses between the first day and 56 days later, as mentioned above.<\/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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\u201cThe models revealed differences in the statistical significance and relative predictive value of risk factors between older and younger patients, including age, BMI, vital signs, and laboratory results,\u201d the study explained.<\/p>

Likewise, variables such as gender and chronic comorbidities in hospitalized patients had a lower predictive value for the models than variables such as vital signs and laboratory results.<\/p>

Another key finding of the research was that vital signs and laboratory results at admission were more important for prediction than the presence of comorbidities. Similarly, age-stratified models showed that the importance of the risk factor differs between young adults and older adults.<\/p>

You can read the full investigation at the following link:<\/p>

https:\/\/www.nature.com\/articles\/s41598-022-10344-3<\/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\/s41598-022-10344-3#<\/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":"

Nueva investigaci\u00f3n mostr\u00f3 modelos de riesgo basados en aprendizaje autom\u00e1tico que indican signos vitales, resultados ex\u00e1menes de laboratorio y edad como factores asociados con casos severos de COVID-19. La revista Scientific Reports de Nature, public\u00f3 el art\u00edculo “Los factores de riesgo de COVID-19 grave difieren seg\u00fan la edad de los adultos hospitalizados”, el cual utiliz\u00f3 modelos de riesgo basados en aprendizaje autom\u00e1tico, para descubrir que los signos vitales, resultados de ex\u00e1menes de laboratorio y la edad, se correlacionaron con el nivel de complicaci\u00f3n de la infecci\u00f3n por COVID-19. El estudio analiz\u00f3 datos de 6,906 adultos que fueron hospitalizados por COVID-19, en cinco estados de Estados Unidos. En este sentido, desarrollaron modelos de riesgo para predecir el uso de ventilaci\u00f3n mec\u00e1nica o la muerte del paciente entre el primer d\u00eda y el d\u00eda 56 de hospitalizaci\u00f3n. En total, para el estudio fueron entrenados cinco modelos de aprendizaje autom\u00e1tica para poblaci\u00f3n de todas las edades y para otras dos subpoblaciones de edades diferentes, 18 a\u00f1os o m\u00e1s y mayores de 50 a\u00f1os. Adem\u00e1s, los modelos de riesgo fueron entrenados a trav\u00e9s de datos cl\u00ednicos f\u00e1cilmente disponibles. De esta forma, fueron utilizados los datos de referencia disponibles una hora despu\u00e9s de la admisi\u00f3n de los pacientes adultos al hospital, as\u00ed como la primera prueba positiva de COVID-19, los cuales fueron vitales para la predicci\u00f3n de enfermedades cr\u00edticas entre el primer d\u00eda y 56 d\u00edas despu\u00e9s, como se mencion\u00f3 anteriormente. \u201cLos modelos revelaron diferencias en la significaci\u00f3n estad\u00edstica y el valor predictivo relativo de los factores de riesgo entre pacientes mayores y m\u00e1s j\u00f3venes, incluida la edad, el IMC, los signos vitales y los resultados de laboratorio\u201d, explic\u00f3 el estudio. Asimismo, las variables como sexo y comorbilidades cr\u00f3nicas en pacientes hospitalizados tuvieron un menor valor predictivo para los modelos que variables como signos vitales y resultados de laboratorio. Otro de los hallazgos clave de la investigaci\u00f3n, fue que los signos vitales y los resultados de laboratorio en el momento de ingreso, fueron m\u00e1s importantes para la predicci\u00f3n que la presencia de comorbilidades. De igual forma, los modelos estratificados por edad, mostraron que la importancia del factor de riesgo difiere entre adultos j\u00f3venes y adultos mayores. Puedes leer la investigaci\u00f3n completa en el siguiente enlace: https:\/\/www.nature.com\/articles\/s41598-022-10344-3 BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41598-022-10344-3#<\/p>","protected":false},"author":1,"featured_media":25113,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,3400,3393,160],"tags":[145],"class_list":["post-25096","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-diagnostico","category-inteligencia-artificial-y-ciencia","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/25096","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=25096"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/25096\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/25113"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=25096"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=25096"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=25096"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}