{"id":13150,"date":"2001-04-10T00:00:00","date_gmt":"2001-04-10T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/utilidad-de-la-inteligencia-artificial-para-predecir-el-riesgo-de-la-reseccion-pulmonar\/"},"modified":"2001-04-10T00:00:00","modified_gmt":"2001-04-10T00:00:00","slug":"utilidad-de-la-inteligencia-artificial-para-predecir-el-riesgo-de-la-reseccion-pulmonar","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/ciencias-medicas\/utilidad-de-la-inteligencia-artificial-para-predecir-el-riesgo-de-la-reseccion-pulmonar\/","title":{"rendered":"Utilidad de la inteligencia artificial para predecir el riesgo de la resecci\u00f3n pulmonar"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Nuria Novoa Valentin <\/strong><\/h2>\n<p>Introducci\u00f3n  la cirug\u00eda de resecci\u00f3n pulmonar en pacientes con carcinoma de pulm\u00f3n no est\u00e1 exenta de riesgo. De momento no contamos con ninguna herramienta que nos permita personalizar el riesgo de cada enfermo. La metodolog\u00eda empleada para su estudio deja varios problemas sin resolver como son el que un porcentaje elevado del riesgo sea impredecible y sobre todo que no se detectan los enfermos que se van a complicar. La inteligencia aritificial (a.I.) Por medio de las redes neuronales puede ser un m\u00e9todo alternativo para el estudio del riesgo que no se ha aplicado antes en cirug\u00eda tor\u00e1cica.  hip\u00f3tesis  la aplicaci\u00f3n de un sistema de a.I.U., A la predicci\u00f3n del riesgo quir\u00fargico de la resecci\u00f3n pulmonar permite mejorar los resultados obtenidos utilizando la metodolog\u00eda de modelizaci\u00f3n mediante regresi\u00f3n log\u00edstica.  m\u00e9todo  estudio prospectivo. Poblaci\u00f3n: pacientes conresecci\u00f3n pulmonar intervenidos en nuestra unidad del el 1\/1\/94 al 311\/12\/00. Divididos en dos grupos: g.De referencia: 1\/1\/94 a l 31\/12\/99 y g.De validaci\u00f3n: 1\/1\/00 al 31\/12\/00. registro de martalidad y de morbilidad de la serie. Estudio descriptivo de variables cont\u00ednuas y cualitativas. Construcci\u00f3n del modelo deregresi\u00f3n log\u00edstica: an\u00e1lisis univariable, multivariable, funci\u00f3n de regresi\u00f3n, validaci\u00f3n del modelo: curva roc, sensibilidad y especificidad. Construcci\u00f3n del modelo de red neuronal. Red perceptron multicapa, definici\u00f3n de variables de trabajo, entrenamiento de la red, validaci\u00f3n: curva roc, sensibilidad y especificidad.  resultados  poblaci\u00f3n total 512 enfermos. Mortalidad total hospitalaria: 5,6%. Modelo de regresi\u00f3n: s\u00f3lo edady fev1 ppo% significativas. Area definida por la curva roc: 0,670. Modelo de red neujronal: area definida por la curva: 0,988.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Utilidad de la inteligencia artificial para predecir el riesgo de la resecci\u00f3n pulmonar<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Utilidad de la inteligencia artificial para predecir el riesgo de la resecci\u00f3n pulmonar <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Nuria Novoa Valentin <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Salamanca<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 04\/10\/2001<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Gonz\u00e1lo Varela Simo<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: alberto Gomez alonso <\/li>\n<li>Javier L\u00f3pez pujol (vocal)<\/li>\n<li>Jorge Freixinet guillart (vocal)<\/li>\n<li>Jes\u00fas L\u00f3pez fidalgo (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Nuria Novoa Valentin Introducci\u00f3n la cirug\u00eda de resecci\u00f3n pulmonar en pacientes con carcinoma de pulm\u00f3n no est\u00e1 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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