{"id":23766,"date":"2022-01-31T08:52:33","date_gmt":"2022-01-31T14:52:33","guid":{"rendered":"https:\/\/saluddigital.com\/?p=23766"},"modified":"2025-10-21T10:24:50","modified_gmt":"2025-10-21T16:24:50","slug":"nuevo-modelo-de-inteligencia-artificial-identifica-a-pacientes-con-riesgo-de-corazon","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/nuevo-modelo-de-inteligencia-artificial-identifica-a-pacientes-con-riesgo-de-corazon\/","title":{"rendered":"New Artificial Intelligence model identifies patients at risk of heart disease complications"},"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

Scientists at the University of Utah developed an Artificial Intelligence (AI) model that could predict the development and complications of patients with cardiovascular problems.<\/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

The model was described in a study published in the first issue of the online scientific journal PLOS Digital Health<\/em>, under the title: An Explainable Artificial Intelligence Approach to Predict Cardiovascular Outcomes Using Electronic Health Records.<\/p>

The study proposes a new method to identify comorbidities in an automated and scalable way called Poisson Binomial Based Comorbidity Discovery (PBC). The method consisted of the analysis of the electronic medical record (EHR) of more than 1.6 million patients from the University of Utah and the Intermountain Primary Children's Hospital.<\/p>

EHR information included data on comorbid diagnoses, procedures, and medications. These data were classified and focused on key areas of cardiovascular health, such as heart transplantation, sinoatrial node dysfunction and various forms of congenital heart disease, which resulted in a "multimorbidity network", which through a combination of variables determined the risk of suffering from a heart condition.<\/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

In the adult population, the study found that people with a previous diagnosis of cardiomyopathy had an 86 higher risk of needing a heart transplant than people who did not have the condition. The study also showed that people with viral myocarditis had a 60-fold increased risk of needing a heart transplant.<\/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

On the other hand, regarding the use of medications, the researchers determined that milrinone, a drug for the treatment of heart failure multiplied by 175 the risk of transplantation. Josh Bonkowsky, director of Intermountain Primary Children's Hospital, explained: "This new technology shows that we can accurately estimate the risk of medical complications and even determine which medications are best for individual patients."<\/p>

"The ability to transform huge EHR collections into compact, portable tools without protected health information solves many of the legal, technological, and data science challenges associated with large-scale EHR analytics," the study explains.<\/p>

The study authors for their part explained that a comprehensive approach like this can help a specialist in the prevention and treatment of serious heart problems. Even though the study focused only on cardiovascular diseases, the researchers explained that the model can be replicated in other areas of medicine and predict the risk of cancer, thyroid surgery, diabetes, for example.<\/p>

"We can use AI to help refine the risk of virtually all medical diagnoses," explained Martin Tristani-Firouzi, one of the study's authors.<\/p>

Check the full study at the following link:<\/p>

https:\/\/journals.plos.org\/digitalhealth\/article?id=10.1371\/journal.pdig.0000004<\/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

\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

UNIVERSITY OF UTAH<\/strong><\/p>

https:\/\/attheu.utah.edu\/facultystaff\/artificial-intelligence-identifies-individuals-at-risk-for-heart-disease-complications\/<\/a><\/p>

PLOS ONE<\/strong><\/p>

https:\/\/journals.plos.org\/digitalhealth\/article?id=10.1371\/journal.pdig.0000004<\/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":"

Cient\u00edficos de la Universidad de Utah desarrollaron un modelo de Inteligencia Artificial (IA) que podr\u00eda predecir el desarrollo y complicaciones de pacientes con problemas cardiovasculares. El modelo fue descrito en un estudio publicado en el primer n\u00famero de la revista cient\u00edfica en l\u00ednea PLOS Digital Health, bajo el t\u00edtulo: Un enfoque de inteligencia artificial explicable para predecir resultados cardiovasculares utilizando registros de salud electr\u00f3nicos. El estudio plantea un nuevo m\u00e9todo para identificar comorbilidades de manera automatizada y escalable llamado Descubrimiento de comorbilidad basado en la binomial de Poisson (PBC). El m\u00e9todo consisti\u00f3 en el an\u00e1lisis de la historia cl\u00ednica electr\u00f3nica (HCE) de m\u00e1s de 1.6 millones de pacientes de la Universidad de Utah y el Intermountain Primary Children\u2019s Hospital. La informaci\u00f3n de las HCE, incluy\u00f3 datos sobre diagn\u00f3sticos, procedimientos y medicamentos com\u00f3rbidos. Estos datos fueron clasificados y enfocados hacia \u00e1reas clave a la salud cardiovascular, como trasplante de coraz\u00f3n, disfunci\u00f3n del n\u00f3dulo sinoauricular y varias formas de enfermedad card\u00edaca cong\u00e9nita, que dio como resultado a una \u201cred de multimorbilidad\u201d, que a trav\u00e9s de combinaci\u00f3n de variables determin\u00f3 el riesgo de padecer alguna afecci\u00f3n cardiaca. En la poblaci\u00f3n adulta, el estudio encontr\u00f3 que personas con diagn\u00f3stico previo de miocardiopat\u00eda, ten\u00edan un riesgo 86 mayor a necesitar un trasplante de coraz\u00f3n, que las personas que no sufr\u00edan de esa condici\u00f3n. Asimismo, el estudio mostr\u00f3 que las persona con miocarditis viral ten\u00edan un riesgo 60 veces mayor a necesitar un trasplante de coraz\u00f3n. Por otra parte, en cuanto al uso de medicamentos, los investigadores determinaron que milrinona, un f\u00e1rmaco para el tratamiento de insuficiencia cardiaca multiplic\u00f3 por 175 el riesgo de trasplante. Josh Bonkowsky, director del Intermountain Primary Children\u2019s Hospital, explic\u00f3 que: \u201cEsta nueva tecnolog\u00eda demuestra que podemos estimar el riesgo de complicaciones m\u00e9dicas con precisi\u00f3n e incluso determinar los medicamentos que son mejores para pacientes individuales\u201d. \u201cLa capacidad de transformar enormes colecciones de EHR en herramientas compactas y port\u00e1tiles sin informaci\u00f3n de salud protegida resuelve muchos de los desaf\u00edos legales, tecnol\u00f3gicos y cient\u00edficos de datos asociados con los an\u00e1lisis de EHR a gran escala\u201d, explica el estudio. Los autores del estudio por su parte, explicaron que un enfoque integral como este puede ayudar a un especialista en la prevenci\u00f3n y tratamiento de problemas card\u00edacos graves. Incluso, aunque el estudio se centr\u00f3 solo en enfermedades cardiovasculares, los investigadores explicaron que el modelo puede replicarse en otras \u00e1reas de la medicina y predecir el riesgo de c\u00e1ncer, cirug\u00eda de tiroides, diabetes, por ejemplo. \u201cPodemos recurrir a la IA para ayudar a refinar el riesgo de pr\u00e1cticamente todos los diagn\u00f3sticos m\u00e9dicos\u201d, explic\u00f3 Martin Tristani-Firouzi, uno de los autores del estudio. Consulta el estudio completo en el siguiente enlace: https:\/\/journals.plos.org\/digitalhealth\/article?id=10.1371\/journal.pdig.0000004 BIBLIOGRAF\u00cdA UNIVERSITY OF UTAH https:\/\/attheu.utah.edu\/facultystaff\/artificial-intelligence-identifies-individuals-at-risk-for-heart-disease-complications\/ PLOS ONE https:\/\/journals.plos.org\/digitalhealth\/article?id=10.1371\/journal.pdig.0000004<\/p>","protected":false},"author":1,"featured_media":23768,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,3393,160],"tags":[145],"class_list":["post-23766","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\/23766","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=23766"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/23766\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/23768"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=23766"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=23766"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=23766"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}