Implementación de un prototipo de software para predecir complicaciones en pacientes en unidad de cuidados intensivos pediátricos (UCIP), a través de modelos de aprendizaje automático

dc.contributorNiño Vásquez, Luis Fernando
dc.contributorIzquierdo Borrero, Ledys María
dc.contributorlaboratorio de Investigación en Sistemas Inteligentes Lisi
dc.creatorBaquero Tibocha, Diego Andrés
dc.date.accessioned2023-05-25T19:48:30Z
dc.date.available2023-05-25T19:48:30Z
dc.date.created2023-05-25T19:48:30Z
dc.date.issued2023
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/83874
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.description.abstractLos cuidadores de pacientes en estado crítico no siempre tienen las habilidades o la experiencia para tratar este tipo de pacientes (Wheatley, 2006). Además, el deterioro fisiológico se puede detectar a partir de cambios sutiles en los signos vitales dentro de una Unidad de Cuidados Intensivos Pediátricos (UCIP) (Izquierdo, 2021). Esto conlleva dificultades para el personal médico al realizar un pronóstico sobre una futura complicación. De acuerdo con lo anterior, este estudio tiene como objetivo implementar un prototipo de software capaz de predecir estados fisiológicos a través de los signos vitales, siguiendo como metodología el Proceso de Aprendizaje Automático (Machine Learning Process, MLP) sobre el conjunto de datos seleccionado. El prototipo se implementó de forma exitosa y se obtuvieron resultados prometedores en cuanto al uso de técnicas de aprendizaje automático para representar el estado actual y futuro de los pacientes en UCIP. Por lo tanto, se debe seguir trabajando en el esfuerzo de complementar el conjunto de datos e implementar nuevas propuestas del uso de las técnicas de aprendizaje automático, y así lograr una monitorización constante sobre los pacientes (Texto tomado de la fuente)
dc.description.abstractCaregivers of critically ill patients do not always have the skills or experience to treat these types of patients (Wheatley, 2006). Furthermore, physiological deterioration can be detected from subtle changes in vital signs within a Pediatric Intensive Care Unit (PICU) (Izquierdo, 2021). This leads to difficulties for medical personnel when making a prognosis about a future complication. Accordingly, this study aims to implement a software prototype capable of predicting physiological states through vital signs, following the Machine Learning Process (MLP) methodology on the selected data set. The prototype was successfully implemented, and promising results were obtained regarding the use of machine learning techniques to represent the current and future state of PICU patients. Therefore, work must continue in the effort to complement the data set and implement new methods for the use of machine learning techniques, and thus achieve constant monitoring of patients.
dc.languagespa
dc.publisherUniversidad Nacional de Colombia
dc.publisherBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisherFacultad de Ingeniería
dc.publisherBogotá, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
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dc.rightsAtribución-NoComercial 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleImplementación de un prototipo de software para predecir complicaciones en pacientes en unidad de cuidados intensivos pediátricos (UCIP), a través de modelos de aprendizaje automático
dc.typeTrabajo de grado - Maestría


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