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dc.contributor.advisorHincapié Díaz, Gustavo Adolfospa
dc.contributor.authorCadavid Ramírez, Jose Rubén
dc.contributor.authorCastañeda Barbosa, Joaquín Camilo
dc.contributor.otherBastidas Goyes, Alirio Rodrigospa
dc.coverage.spatialMedicinaspa
dc.date.accessioned2020-03-10T19:53:23Z
dc.date.available2020-03-10T19:53:23Z
dc.date.issued2020-02-03
dc.identifier.urihttp://hdl.handle.net/10654/35075
dc.description.abstractEn la actualidad se dispone de varios puntajes para la predicción de desenlaces como mortalidad en la exacerbación aguda de la EPOC, estos puntajes o escalas son útiles para la toma de decisiones disminuyendo el grado de incertidumbre al que el médico se enfrenta cuando aborda el problema del pronóstico del paciente, sin embargo, su utilización es poco frecuente y algunas de ellas requieren paraclínicos que a pesar de ser básicos pueden no encontrase disponibles en todas las ocasiones. En los últimos años, los avances de programación en software, han permitido el desarrollo de metodologías, que simulando el comportamiento del cerebro humano pueden generar soluciones económicas y altamente confiables a los problemas de incertidumbre como es el caso de pronóstico médico. Se desarrollará un estudio de pronóstico con una red neuronal tipo perceptrón multicapa para la predicción de los desenlaces de ventilación mecánica y muerte en la exacerbación aguda de la EPOC comparándose sus resultados con los puntajes DECAF, BAP-65 y CURB-65.spa
dc.description.tableofcontentsTabla de contenido Introducción 5 Pregunta de investigación 5 Justificación 5 Marco teórico: 6 Definiciones 6 Epidemiología: 7 Factores pronósticos para la exacerbación 7 Etiología: 7 Patogénesis: 8 Historia clínica y examen físico: 8 Diagnóstico: 8 Tratamiento: 8 Prevención: 9 Pronóstico: 9 Puntajes utilizados: 9 CURB-65: 9 DECAF: 9 BAP-65: 10 Otros Scores: 10 Redes neuronales artificiales: 10 Objetivos 11 Objetivo general: 11 Objetivos específicos: 11 Materiales y Métodos: 12 Tipo de diseño: 12 Definición de exacerbación de la EPOC: 12 Definición de desenlaces para ser pronosticados durante la exacerbación: 12 4 Determinación de desenlaces en la exacerbación de la EPOC por la red neuronal: 13 Población a estudio: 13 Descripción de la metodología. 13 Tamaño de muestra y aleatorización: 13 Criterios de selección: 13 Criterios de inclusión: 13 Criterios de exclusión: 13 Recolección de datos: 14 Control de sesgos y error: 14 Conducción del estudio 14 Consideraciones éticas 15 Resultados: 15 Resultados análisis multivariado regresión logística 28 Resultados de validez y área bajo la curva de características operativas de receptor: 31 Discusión: 37 Conclusiones: 41 Referencias: 42spa
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dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.language.isospaspa
dc.publisherUniversidad Militar Nueva Granadaspa
dc.rightsDerechos Reservados - Universidad Militar Nueva Granada, 2020spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.5/co/spa
dc.titleRendimiento de redes neuronales artificiales para la predicción de desenlaces en exacerbación aguda de la EPOC.spa
dc.typeinfo:eu-repo/semantics/bachelorThesisspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.publisher.departmentFacultad de Medicinaspa
dc.type.localTrabajo de gradospa
dc.description.abstractenglishToday´s date, there are several scales for prediction of outcomes regarding chronic obstructive pulmonary disease (COPD), which using clinical and paraclinical variables allow classification and subsequent decision making. The dependence of these scales of variables on paraclinics means that it is not always possible to carry them out. Therefore, this study aims to demonstrate the usefulness of a multi-layer perceptron artificial neural network for classification. Using two prospective cohorts of previous studies of patients from the central military hospital where performance of the different scales was assessed (DECAF, CRB 65, CURB 65, BAP 65 Anthonisen), supervised learning of the artificial neural network was performed to evaluate performance assessed to mortality and mechanical ventilation. A total of 1478 acute exacerbations of COPD were analyzed. In the first cohort, mortality was found in 4.3%, and the requirement of mechanical ventilation in 31.9%, in the second cohort, mortality was 7.4. % and mechanical ventilation of 31.6%, and in the validation cohort there was a 7-day mortality of 2.6%, a 30-day mortality of 5.8% and mechanical ventilation of 14.3%. When compared with prognostic scales commonly used in this pathology, it has been found that its diagnostic performance is similar or superior to that of the diagnostic scales even with the use of fewer variables, possibly due to the ability of the artificial neural network to stratify with greater or lower severity of patients according to the degree of multi-organic commitment of the disease.eng
dc.title.translatedPerformance of an artificial neuronal network for prediction of outcomes in acute excerbation of COPD.spa
dc.subject.keywordsCOPDspa
dc.subject.keywordsDisease Progressionspa
dc.subject.keywordsPrognosisspa
dc.subject.keywordsArtificial Neuronal Networksspa
dc.publisher.programMedicina internaspa
dc.creator.degreenameEspecialista en Medicina internaspa
dc.subject.decsMEDICINA INTERNA
dc.subject.decsENFERMEDAD PULMONAR OBSTRUCTIVA CRONICA
dc.subject.decsENFERMEDADES OBSTRUCTIVAS DE LOS PULMONES
dc.subject.decsREDES NEURALES (COMPUTADORES)
dc.contributor.corporatenameUNIVERSIDAD MILITAR NUEVA GRANADAspa
dc.description.degreelevelEspecializaciónspa
dc.publisher.facultyMedicina y Ciencias de la Salud - Medicina internaspa
dc.type.dcmi-type-vocabularyTextspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadasspa
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dc.subject.proposalEPOCspa
dc.subject.proposalExacerbación agudaspa
dc.subject.proposalRedes Neuronalesspa


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Derechos Reservados - Universidad Militar Nueva Granada, 2020
Except where otherwise noted, this item's license is described as Derechos Reservados - Universidad Militar Nueva Granada, 2020