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.contributor.advisor | Niño Vásquez, Luis Fernando | |
| dc.contributor.advisor | Izquierdo Borrero, Ledys María | |
| dc.contributor.author | Baquero Tibocha, Diego Andrés | |
| dc.contributor.researchgroup | laboratorio de Investigación en Sistemas Inteligentes Lisi | spa |
| dc.date.accessioned | 2023-05-25T19:48:30Z | |
| dc.date.available | 2023-05-25T19:48:30Z | |
| dc.date.issued | 2023 | |
| dc.description | ilustraciones, graficas | spa |
| dc.description.abstract | Los 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) | spa |
| dc.description.abstract | Caregivers 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. | eng |
| dc.description.degreelevel | Maestría | spa |
| dc.description.degreename | Magíster en Ingeniería - Ingeniería de Sistemas y Computación | spa |
| dc.description.researcharea | Sistemas inteligentes | spa |
| dc.description.technicalinfo | Para la implementación de esta aplicación del aprendizaje automático a la medicina y su interacción con el personal médico, se diseñó una plataforma que se divide en tres aplicaciones llamadas: Frontend (JavaScript/ReactJS), Backend (.NET Ciore 5), y Model API (Python/Keras/Scikit-learn). Las aplicaciones interoperan entre sí para recibir las entradas del usuario, leer los datos, y generar las clasificaciones y predicciones, respectivamente. Las aplicaciones fueron desplegadas en la nube a través de los servicios de Azure y fue necesaria la implementación de Docker para el despliegue del módulo Model API. | spa |
| dc.format.extent | xiv, 87 páginas | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.instname | Universidad Nacional de Colombia | spa |
| dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia | spa |
| dc.identifier.repourl | https://repositorio.unal.edu.co/ | spa |
| dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/83874 | |
| dc.language.iso | spa | spa |
| dc.publisher | Universidad Nacional de Colombia | spa |
| dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | spa |
| dc.publisher.faculty | Facultad de Ingeniería | spa |
| dc.publisher.place | Bogotá, Colombia | spa |
| dc.publisher.program | Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación | spa |
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| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.license | Atribución-NoComercial 4.0 Internacional | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | spa |
| dc.subject.ddc | 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería | spa |
| dc.subject.lemb | APRENDIZAJE | spa |
| dc.subject.lemb | Learning | eng |
| dc.subject.lemb | APRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL) | spa |
| dc.subject.lemb | Machine learning | eng |
| dc.subject.proposal | Signos vitales | spa |
| dc.subject.proposal | Aprendizaje automático | spa |
| dc.subject.proposal | Predicción | spa |
| dc.subject.proposal | Estado clínico | spa |
| dc.subject.proposal | Cuidado intensivo pediátrico | spa |
| dc.subject.proposal | Vital signs | eng |
| dc.subject.proposal | Prediction | eng |
| dc.subject.proposal | Clinical status | eng |
| dc.subject.proposal | Pediatric intensive care | eng |
| dc.title | 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 | spa |
| dc.title.translated | Implementation of a software prototype to predict complications in patients in a pediatric intensive care unit (PICU), through machine learning models | eng |
| dc.type | Trabajo de grado - Maestría | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
| dc.type.redcol | http://purl.org/redcol/resource_type/TM | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
| dcterms.audience.professionaldevelopment | Público general | spa |
| oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
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