{"id":21605,"date":"2021-10-06T08:50:22","date_gmt":"2021-10-06T13:50:22","guid":{"rendered":"https:\/\/saluddigital.com\/?p=21605"},"modified":"2025-10-21T11:55:21","modified_gmt":"2025-10-21T17:55:21","slug":"cientificos-de-la-unam-uam-y-centro-medico-abc-desarrollaron-algoritmo-que-identifica-pacientes-prioritarios-para-recibir-atencion-medica-por-covid-19","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/cientificos-de-la-unam-uam-y-centro-medico-abc-desarrollaron-algoritmo-que-identifica-pacientes-prioritarios-para-recibir-atencion-medica-por-covid-19\/","title":{"rendered":"Scientists from UNAM, UAM and ABC Medical Center developed an algorithm that identifies priority patients to receive medical care for 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

Researchers from the National Autonomous University of Mexico (UNAM), the Autonomous Metropolitan University (UAM), and the ABC Medical Center have developed an algorithm to identify priority patients for medical care due to 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
\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

Researchers Alfred Barry U'Ren Cort\u00e9s and Roberto de J. Le\u00f3n-Montiel, from the Institute of Nuclear Sciences at UNAM; Mario Alan Quiroz Ju\u00e1rez of the UAM; and Armando Torres G\u00f3mez and Irma Hoyo Ulloa from the ABC Medical Center, developed an algorithm to support health professionals to identify patients who require priority care due to COVID-19.\u00a0<\/p>

The algorithm was tested through research published in the scientific journal PLOS One<\/em>. Research showed up to 93.5% efficiency. U'Ren Cort\u00e9s explained and his colleagues and he saw in machine learning\/machine learning<\/em> an opportunity to speed up the work of doctors during the pandemic, to act more quickly on the care that each patient requires and save lives.<\/p>

In addition, the algorithm was trained using data from the Morbidity Statistical Yearbooks, published by the General Directorate of Epidemiology of the Ministry of Health and gathered information on more than 4.7 million patients who were admitted to the hospital between March 2021.<\/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

\u201cWhen one uses machine learning<\/em> a basic ingredient is to have data of good quality and in sufficient volume. There is a learning phase in which one provides feature sets with an associated outcome, in this case whether the patient survived or died. The information enters the algorithm with the known data in what is called training and, after that, for a new patient, it recognizes the patterns of previous cases and an instant prediction can be produced\u201d, explained U'Ren Cort\u00e9s.<\/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

This tool of machine learning<\/em> It includes information on the patient's medical history, as well as diseases such as diabetes, chronic obstructive pulmonary disease (COPD), hypertension, cardiovascular problems, chronic kidney failure, obesity, or if the patient uses immunosuppressants. In addition, it also considers data such as gender, place of birth, place of residence and age.<\/p>

In total, there are 21 characteristics that help classify patients into two categories: greater chance of recovery and greater chance of dying. These variables are trained through the neural network for each of the four clinical stages: the first stage considers the diseases specified above, as well as age and other basic information; the second stage corresponds to the COVID-19 status, that is, positive or negative, and to COVID-19-related pneumonia; stage three corresponds to the state of hospitalization; and the fourth stage to intensive care and intubation.<\/p>

\u201cIt's a neural network made up of interconnected nodes, and when we pass data, these nodes learn. They are said to learn, but in reality they adjust their parameters so that the information they receive is passed on to them. Once the stage we call training has passed, she can make predictions in the future based on what she has learned,\u201d explained Quiroz Ju\u00e1rez.<\/p>

Currently, the algorithm is being applied to mobile devices in hospitals and incorporate data in real time. On the other hand, the study had financial support from the National Council for Science and Technology (Conacyt).<\/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

UNAM GAZETTE<\/strong><\/p>

https:\/\/www.gaceta.unam.mx\/con-algoritmo-identifican-a-pacientes-vulnerables\/<\/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":"

Investigadores de la Universidad Nacional Aut\u00f3noma de M\u00e9xico (UNAM), Universidad Aut\u00f3noma Metropolitana (UAM) y el Centro M\u00e9dico ABC han desarrollado un algoritmo para identificar pacientes prioritarios para recibir atenci\u00f3n m\u00e9dica debido a COVID-19. Los investigadores Alfred Barry U\u2019Ren Cort\u00e9s y Roberto de J. Le\u00f3n-Montiel, del Instituto de Ciencias Nucleares de la UNAM; Mario Alan Quiroz Ju\u00e1rez de la UAM; y Armando Torres G\u00f3mez e Irma Hoyo Ulloa del Centro M\u00e9dico ABC, desarrollaron un algoritmo para apoyar a los profesionales de la salud a identificar a pacientes que requieran atenci\u00f3n prioritaria por COVID-19.  El algoritmo fue probado a trav\u00e9s de una investigaci\u00f3n publicada en la revista cient\u00edfica PLOS One. La investigaci\u00f3n mostr\u00f3 hasta un 93.5% de eficiencia. U\u2019Ren Cort\u00e9s, explic\u00f3 y sus colegas y \u00e9l vieron en el aprendizaje autom\u00e1tico\/machine learning una oportunidad para agilizar el trabajo de los m\u00e9dicos durante la pandemia, para accionar m\u00e1s r\u00e1pido a las atenciones que requiere cada paciente y salvar vidas. Adem\u00e1s, el algoritmo fue entrenado a partir de datos de los Anuarios Estad\u00edsticos de Morbilidad, publicados por la Direcci\u00f3n General de Epidemiolog\u00eda de la Secretar\u00eda de Salud y reuni\u00f3 informaci\u00f3n de m\u00e1s de 4.7 millones de pacientes que ingresaron al hospital entre marzo de 2021. \u201cCuando uno utiliza machine learning un ingrediente b\u00e1sico es tener datos de buena calidad y en suficiente volumen. Hay una fase de aprendizaje en la que uno provee conjuntos de caracter\u00edsticas con un resultado asociado, en este caso, si el paciente sobrevivi\u00f3 o falleci\u00f3. La informaci\u00f3n ingresa al algoritmo con los datos conocidos en lo que se llama entrenamiento y, despu\u00e9s de eso, para un paciente nuevo reconoce los patrones de los casos anteriores y se puede producir una predicci\u00f3n instant\u00e1nea\u201d, explic\u00f3 U\u2019Ren Cort\u00e9s. Esta herramienta de machine learning contempla informaci\u00f3n del historial m\u00e9dico del paciente, as\u00ed como enfermedades como diabetes, enfermedad pulmonar obstructiva cr\u00f3nica (COPD), hipertensi\u00f3n, problemas cardiovasculares, falla renal cr\u00f3nica, obesidad, o si el paciente utiliza inmunosupresores. Adem\u00e1s, tambi\u00e9n considera datos como el sexo, el lugar de nacimiento, lugar de residencia y edad. En total son 21 caracter\u00edsticas que ayudan a una clasificaci\u00f3n de los pacientes en dos categor\u00edas: mayor posibilidad de recuperarse y mayor posibilidad de fallecer. Estas variables se entrenan a trav\u00e9s de la red neuronal para cada una de las cuatro etapas cl\u00ednicas: la primera etapa considera las enfermedades antes especificadas, as\u00ed como la edad y otra informaci\u00f3n b\u00e1sica; la segunda etapa corresponde a el estatus COVID-19, es decir positivo o negativo y a neumon\u00eda relacionada con COVID-19; la etapa tres corresponde al estado de hospitalizaci\u00f3n; y la cuarta etapa a cuidados intensivos y a intubaci\u00f3n. \u201cEs una red neuronal compuesta por nodos interconectados y, cuando hacemos pasar datos, estos nodos aprenden. Se dice que aprenden, pero en realidad ajustan sus par\u00e1metros de manera que la informaci\u00f3n que reciben se herede en ellos. Una vez que ha pasado la etapa que llamamos entrenamiento, ella pueda hacer predicciones en el futuro a partir de lo que aprendi\u00f3\u201d, explico Quiroz Ju\u00e1rez. Actualmente se busca que el algoritmo se aplique en dispositivos m\u00f3viles de hospitales e incorporar datos en tiempo real.  Por otra parte, el estudio cont\u00f3 con apoyo econ\u00f3mico por parte del Consejo Nacional de Ciencia y Tecnolog\u00eda (Conacyt). BIBLIOGRAF\u00cdA GACETA UNAM https:\/\/www.gaceta.unam.mx\/con-algoritmo-identifican-a-pacientes-vulnerables\/<\/p>","protected":false},"author":1,"featured_media":21607,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,3393,160],"tags":[145],"class_list":["post-21605","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-inteligencia-artificial-y-ciencia","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/21605","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=21605"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/21605\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/21607"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=21605"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=21605"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=21605"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}