{"id":28367,"date":"2022-07-07T06:34:47","date_gmt":"2022-07-07T11:34:47","guid":{"rendered":"https:\/\/saluddigital.com\/?p=28367"},"modified":"2025-10-20T11:35:29","modified_gmt":"2025-10-20T17:35:29","slug":"mayo-clinic-desarrolla-modelo-de-aprendizaje-automatico-para-diferenciar-enfermedades","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/mayo-clinic-desarrolla-modelo-de-aprendizaje-automatico-para-diferenciar-enfermedades\/","title":{"rendered":"Mayo Clinic develops machine learning model to differentiate diseases"},"content":{"rendered":"
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Mayo Clinic researchers developed a machine learning model to differentiate patients with acute cholangitis and hepatitis associated with alcoholism.\t<\/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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A study presented by the Mayo Clinic showed how a machine learning model can support medical decision-making by helping specialists differentiate between alcohol-related hepatitis (AH) and acute cholangitis (AC) using simple laboratory variables.<\/p>

Despite the differences of both conditions, the clinical similarities can complicate the work of doctors due to the symptoms presented. The study included 459 adult participants, who attended the Mayo Clinic, Rochester, of whom 265 had AH and 194 AC.<\/p>

"This study was motivated by seeing many medical providers in the emergency department or ICU struggle to distinguish between acute cholangitis and alcohol-associated hepatitis, which are very different conditions that can present in similar ways," explained Joseph Ahn, a gastroenterologist at Mayo Clinic, Rochester.<\/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 analysis took into account ten laboratory variables: white blood cell count, hemoglobin, mean corpuscular volume, platelet count, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, total bilirubin, direct bilirubin, and albumin. As well as eight machine learning models such as decision tree, naive Bayes, logistic regression, artificial neural networks, among others.<\/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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"We developed and trained machine learning algorithms to distinguish the two conditions using some of the routinely available lab values that all these patients should have," explained Dr. Ahn.<\/p>

In this way, the models were trained with these laboratory variables. Machine learning models demonstrated accurate returns with 0.923 and area on the curve above 0.986. That is, the validation obtained a precision 93%. It also complied with external validations and was accurate using another patient database, however, the accuracy percentage decreased by three points.<\/p>

The study highlighted the use and clinical potential of using trained machine learning algorithms. In this way, in addition to achieving greater precision for the diagnosis of these conditions, patients would not have to undergo invasive procedures, which can delay diagnosis.<\/p>

Check out the full article here: https:\/\/secure.jbs.elsevierhealth.com\/pb-assets\/Health%20Advance\/journals\/jmcp\/JMCP3718_proof.pdf<\/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

ELSEVIER HEALTH<\/strong><\/p>

https:\/\/secure.jbs.elsevierhealth.com\/pb-assets\/Health%20Advance\/journals\/jmcp\/JMCP3718_proof.pdf<\/a><\/p>

MAYO CLINIC<\/strong><\/p>

https:\/\/newsnetwork.mayoclinic.org\/discussion\/machine-learning-algorithms-can-help-health-care-staff-correctly-diagnose-alcohol-associated-hepatitis-acute-cholangitis\/<\/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 Mayo Clinic desarrollaron un modelo de aprendizaje autom\u00e1tico para diferenciar a pacientes con colangitis aguda y hepatitis asociada con alcoholismo. Un estudio presentado por Mayo Clinic mostr\u00f3 c\u00f3mo un modelo de aprendizaje autom\u00e1tico puede apoyar en la toma de decisiones m\u00e9dicas, ayudando a los especialistas a diferenciar hepatitis relacionada con alcohol (AH) y colangitis aguda (AC), utilizando variables simples de laboratorio. A pesar de las diferencias de ambas condiciones, las similitudes cl\u00ednicas pueden complicar el trabajo de los m\u00e9dicos debido los s\u00edntomas presentados. El estudio incluy\u00f3 a 459 participantes adultos, que acudieron presencialmente a Mayo Clinic, Rochester, de los cuales 265 padec\u00edan AH y 194 AC. \u201cEste estudio fue motivado por ver a muchos proveedores m\u00e9dicos en el departamento de emergencias o UCI luchar para distinguir la colangitis aguda y la hepatitis asociada al alcohol, que son condiciones muy diferentes que pueden presentarse de manera similar\u201d, explic\u00f3 Joseph Ahn, gastroenter\u00f3logo de Mayo Clinic, Rochester. El an\u00e1lisis tom\u00f3 en cuenta diez variables de laboratorio: conteo de gl\u00f3bulos blancos, hemoglobina, volumen corpuscular medio, conteo de plaquetas, aspartato aminotransferasa, alanina aminotransferasa, fosfatasa alcalina, bilirrubina total, bilirrubina directa y alb\u00famina. As\u00ed como ocho modelos de aprendizaje autom\u00e1tica como \u00e1rbol de decisi\u00f3n, naive Bayes, regresi\u00f3n log\u00edstica, redes neuronales artificiales, entre otras. \u201cDesarrollamos y entrenamos algoritmos de aprendizaje autom\u00e1tico para distinguir las dos condiciones utilizando algunos de los valores de laboratorio disponibles de forma rutinaria que todos estos pacientes deber\u00edan tener\u201d, explic\u00f3 el Dr. Ahn. De esta forma los modelos fueron entrenados con dichas variables de laboratorio. Los modelos de aprendizaje autom\u00e1tico demostraron rendimientos precisos con 0.923 y \u00e1rea sobre la curva arriba de 0.986. Es decir, la validaci\u00f3n obtuvo un 93% de precisi\u00f3n.\u00a0 Asimismo, cumpli\u00f3 con las validaciones externas y fue precisa utilizando otra base de datos de pacientes, sin embargo, el porcentaje de precisi\u00f3n disminuy\u00f3 en tres puntos. El estudio destac\u00f3 el uso y el potencial cl\u00ednico del uso de algoritmos entrenados de aprendizaje autom\u00e1tico. De esta forma adem\u00e1s de lograr mayor precisi\u00f3n para el diagn\u00f3stico de estas condiciones, los pacientes no tendr\u00edan que someterse a procedimientos invasivos, que pueden retrasar el diagn\u00f3stico. Consulta el art\u00edculo completo aqu\u00ed: https:\/\/secure.jbs.elsevierhealth.com\/pb-assets\/Health%20Advance\/journals\/jmcp\/JMCP3718_proof.pdf BIBLIOGRAF\u00cdA ELSEVIER HEALTH https:\/\/secure.jbs.elsevierhealth.com\/pb-assets\/Health%20Advance\/journals\/jmcp\/JMCP3718_proof.pdf MAYO CLINIC https:\/\/newsnetwork.mayoclinic.org\/discussion\/machine-learning-algorithms-can-help-health-care-staff-correctly-diagnose-alcohol-associated-hepatitis-acute-cholangitis\/<\/p>","protected":false},"author":1,"featured_media":28369,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,3400,160],"tags":[145],"class_list":["post-28367","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-diagnostico","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/28367","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=28367"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/28367\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/28369"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=28367"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=28367"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=28367"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}