{"id":13633,"date":"2021-04-16T09:48:29","date_gmt":"2021-04-16T14:48:29","guid":{"rendered":"https:\/\/saluddigital.com\/?p=13633"},"modified":"2025-10-21T12:49:08","modified_gmt":"2025-10-21T18:49:08","slug":"aprendizaje-automatico-para-pacientes-en-riesgo-de-infarto-uso-de-aprendizaje-automatico-con-datos-de-expediente-clinico-electronico-para-tratar-pacientes-con-condiciones-cardiovasculares","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/aprendizaje-automatico-para-pacientes-en-riesgo-de-infarto-uso-de-aprendizaje-automatico-con-datos-de-expediente-clinico-electronico-para-tratar-pacientes-con-condiciones-cardiovasculares\/","title":{"rendered":"Machine Learning for patients at risk of heart attack"},"content":{"rendered":"
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A study on the use of machine learning, via the electronic medical record (EMR) of patients with cardiovascular conditions, was published in Nature.<\/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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The EMR contains information about patients' physiological aspects and information. When the physiological level reaches a critical point that requires examination, there are beliefs of the specialist medical staff that serve in decision making.<\/p>

\u00a0Two types of data are obtained through machine learning: first, those related to specific actions of the medical practice, expressions in decision-making, diagnoses, referrals, consultations or non-routine procedures; then those data \"not initiated by the physician\", which include physiological orders and intakes of the patient.<\/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 study explains that, through machine learning models, it is possible to improve the diagnosis or prognosis of a disease, because it seeks to be based on evidence of the patient's physiology and condition, rather than on medical beliefs.<\/p>

The authors explain that the usefulness of the model seeks to be effective in reducing hospital mortality and does not seek to predict the behavior of the disease.<\/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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Finally, the authors detail the following in the Discussion section: \u201cThe results of our experiments indicate machine learning models trained only on clinician-initiated administrative data can currently achieve performance close to models trained on more detailed, complete, EMR data\u201d.<\/p>

You can read the full paper, \u201cMachine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?\u201d via the following link https:\/\/www.nature.com\/articles\/s41746-021-00426-3<\/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\/s41746-021-00426-3<\/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":"

Fue publicado en Nature un estudio sobre el uso de aprendizaje autom\u00e1tico, por medio del expediente cl\u00ednico electr\u00f3nico (ECE) de pacientes con condiciones cardiovasculares. El ECE contiene informaci\u00f3n sobre aspectos e informaci\u00f3n fisiol\u00f3gica de los pacientes. Cuando el nivel fisiol\u00f3gico llega a un punto cr\u00edtico que requiere examinaci\u00f3n, existen creencias del personal m\u00e9dico especialista que sirven en la toma de decisiones.  A trav\u00e9s del aprendizaje autom\u00e1tico se obtienen dos tipos de datos, primero, aquellos relacionados con acciones espec\u00edficas del ejercicio m\u00e9dico, expresiones en la toma de decisiones, diagn\u00f3sticos, referenciales, consultas o procedimientos no rutinarios; posteriormente aquellos datos \u201cno iniciados por el m\u00e9dico\u201d, que incluyen ordenes y tomas fisiol\u00f3gicas del paciente. El estudio explica que, a trav\u00e9s de los modelos de aprendizaje autom\u00e1tico, es posible mejorar el diagn\u00f3stico o pron\u00f3stico de una enfermedad, porque busca basarse en la evidencia de la fisiolog\u00eda y condici\u00f3n del paciente, antes que en las creencias m\u00e9dicas. Los autores explican que la utilidad del modelo busca ser efectivo para reducir mortalidad hospitalaria y no busca predecir el comportamiento de la enfermedad. Finalmente, los autores detallan lo siguiente en el apartado de Discusi\u00f3n: \u201cLos resultados de nuestros experimentos indican que los modelos de aprendizaje autom\u00e1tico entrenados solo con datos administrativos iniciados por el m\u00e9dico pueden lograr actualmente un rendimiento cercano a los modelos entrenados en datos de ECE m\u00e1s detallados y completos\u201d. Puedes leer el art\u00edculo completo: \u201cAprendizaje autom\u00e1tico para la estratificaci\u00f3n del riesgo del paciente: \u00bfpararse o mirar por encima de los hombros de los m\u00e9dicos?\u201d a trav\u00e9s del siguiente enlace https:\/\/www.nature.com\/articles\/s41746-021-00426-3. BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41746-021-00426-3<\/p>","protected":false},"author":1,"featured_media":13634,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,1418],"tags":[145],"class_list":["post-13633","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-big-data","category-inteligencia-artificial-y-ciencia","category-resena-de-publicaciones-cientificas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/13633","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=13633"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/13633\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/13634"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=13633"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=13633"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=13633"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}