{"id":27052,"date":"2022-06-06T08:46:18","date_gmt":"2022-06-06T13:46:18","guid":{"rendered":"https:\/\/saluddigital.com\/?p=27052"},"modified":"2025-10-20T11:51:07","modified_gmt":"2025-10-20T17:51:07","slug":"investigadores-desarrollan-aprendizaje-automatico-para-la-prediccion-de-complicaciones-quirurgicas","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/noticias\/investigadores-desarrollan-aprendizaje-automatico-para-la-prediccion-de-complicaciones-quirurgicas\/","title":{"rendered":"Researchers develop machine learning for prediction of surgical complications"},"content":{"rendered":"
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Researchers at the University of Florida Health (UFHealth) have developed a machine learning model that can help identify and predict complications in surgical interventions.<\/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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Thanks to the development of an Artificial Intelligence (AI) model based on machine learning, UFHealth researchers have confirmed that it is possible to predict surgical complications and better manage these situations. In addition, one of the important aspects is that doctors can receive this information directly on their mobile devices.<\/p>

The researchers explain that the algorithm will show accurate predictions through a mobile app, and will offer professionals the opportunity to consult detailed information on patients and the risk of complications in surgery.<\/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 researchers developed an algorithm called MySurgeryRisk, which is based on machine learning and trained for nearly seven years using more than 74,000 medical procedures on 58,000 adult patients. In this way the model was able to match the surgeons' accuracy in predicting surgical outcomes.<\/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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According to the researchers, the system takes advantage of AI technology to process clinical data in real time, driving what they call "analytic pipeline", which sends the results directly to the mobile devices of professionals. These types of advances benefit surgeons in the acquisition of skills and experience and, above all, improve their decision-making capacity.<\/p>

However, the specialists detailed that this machine learning model is not only useful thanks to its predictions, but also in its way of collecting, compiling and distributing data and information in real time.<\/p>

Researchers are even looking to collect additional data such as information from ventilator monitors and anesthesia devices, so the model can become even more accurate.<\/p>

The original research was published on JAMA Network, you can consult it at the following link: https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/fullarticle\/2792367<\/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

UFHEALTH<\/strong><\/p>

https:\/\/ufhealth.org\/news\/2022\/uf-researchers-artificial-intelligence-platform-accurately-predicts-surgical-complications<\/a><\/p>

JAMA NETWORK<\/strong><\/p>

https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/fullarticle\/2792367<\/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 University of Florida Health (UFHealth), han desarrollado un modelo de aprendizaje autom\u00e1tico que puede ayudar a identificar y predecir complicaciones en intervenciones quir\u00fargicas. Gracias al desarrollo de un modelo de Inteligencia Artificial (IA) basado en aprendizaje autom\u00e1tico, investigadores de UFHealth, han confirmado que es posible predecir complicaciones quir\u00fargicas y manejar de mejor forma estas situaciones. Adem\u00e1s, uno de los aspectos importantes es que los m\u00e9dicos pueden recibir dicha informaci\u00f3n directamente en sus dispositivos m\u00f3viles. Los investigadores explican que el algoritmo mostrar\u00e1 a trav\u00e9s de una app m\u00f3vil las predicciones precisas, y les ofrecer\u00e1 a los profesionales la oportunidad de consultar informaci\u00f3n detallada de los pacientes y el riesgo de complicaci\u00f3n en cirug\u00eda. Los investigadores desarrollaron un algoritmo llamado MySurgeryRisk, que se basa en aprendizaje autom\u00e1tico y fue entrenado durante casi siete a\u00f1os utilizando m\u00e1s de 74 mil procedimientos m\u00e9dicos de 58 mil pacientes adultos. De esta forma el modelo pudo igualar la precisi\u00f3n de los cirujanos en la predicci\u00f3n de resultados quir\u00fargicos. Seg\u00fan explican los investigadores, el sistema aprovecha la tecnolog\u00eda de IA para procesar datos cl\u00ednicos en tiempo real, impulsando lo que llaman “canalizaci\u00f3n anal\u00edtica”, que env\u00eda directamente los resultados a los dispositivos m\u00f3viles de los profesionales. Este tipo de avances benefician a los cirujanos en la adquisici\u00f3n de habilidad y experiencia y sobre todo mejoran su capacidad de toma de decisiones. No obstante, los especialistas detallaron que este modelo de aprendizaje autom\u00e1tico, no solo es de utilidad gracias a sus predicciones, sino tambi\u00e9n en su manera de recolectar, recopilar y distribuir datos e informaci\u00f3n en tiempo real. Incluso los investigadores buscan recolectar datos adicionales como la informaci\u00f3n de los monitores del ventilador y de los dispositivos de anestesia, de esta forma el modelo puede llegar a ser todav\u00eda m\u00e1s preciso. La investigaci\u00f3n original fue publicada en JAMA Network, puedes consultarla en el siguiente enlace: https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/fullarticle\/2792367 BIBLIOGRAF\u00cdA UFHEALTH https:\/\/ufhealth.org\/news\/2022\/uf-researchers-artificial-intelligence-platform-accurately-predicts-surgical-complications JAMA NETWORK https:\/\/jamanetwork.com\/journals\/jamanetworkopen\/fullarticle\/2792367<\/p>","protected":false},"author":1,"featured_media":27054,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3395,3401,160],"tags":[145],"class_list":["post-27052","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-apps-moviles-e-internet-de-las-cosas","category-iotapps","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/27052","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=27052"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/27052\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/27054"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=27052"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=27052"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=27052"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}