Modelamiento del espacio de signos vitales para detectar el deterioro de los pacientes en una unidad de cuidados intensivos

ilustraciones

Autores:
Izquierdo Borrero, Ledys Maria
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/79717
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/79717
https://repositorio.unal.edu.co/
Palabra clave:
610 - Medicina y salud
Cuidados Intensivos
Critical Care
Pediatría
Pediatrics
Signos vitales
modelo oculto de Márkov
cuidado intensivo pediátrico.
inteligencia artificial
Vital signs
Hidden Márkov model
pediatric critical care
Artificial Intelligence
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional
id UNACIONAL2_488444d1b5fbc467d7683427b0fd6d21
oai_identifier_str oai:repositorio.unal.edu.co:unal/79717
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Modelamiento del espacio de signos vitales para detectar el deterioro de los pacientes en una unidad de cuidados intensivos
dc.title.translated.eng.fl_str_mv Modeling the vital sign space to detect the deterioration of patients in a pediatric intensive care unit
title Modelamiento del espacio de signos vitales para detectar el deterioro de los pacientes en una unidad de cuidados intensivos
spellingShingle Modelamiento del espacio de signos vitales para detectar el deterioro de los pacientes en una unidad de cuidados intensivos
610 - Medicina y salud
Cuidados Intensivos
Critical Care
Pediatría
Pediatrics
Signos vitales
modelo oculto de Márkov
cuidado intensivo pediátrico.
inteligencia artificial
Vital signs
Hidden Márkov model
pediatric critical care
Artificial Intelligence
title_short Modelamiento del espacio de signos vitales para detectar el deterioro de los pacientes en una unidad de cuidados intensivos
title_full Modelamiento del espacio de signos vitales para detectar el deterioro de los pacientes en una unidad de cuidados intensivos
title_fullStr Modelamiento del espacio de signos vitales para detectar el deterioro de los pacientes en una unidad de cuidados intensivos
title_full_unstemmed Modelamiento del espacio de signos vitales para detectar el deterioro de los pacientes en una unidad de cuidados intensivos
title_sort Modelamiento del espacio de signos vitales para detectar el deterioro de los pacientes en una unidad de cuidados intensivos
dc.creator.fl_str_mv Izquierdo Borrero, Ledys Maria
dc.contributor.advisor.none.fl_str_mv Niño Vasquez, Luis Fernando
dc.contributor.author.none.fl_str_mv Izquierdo Borrero, Ledys Maria
dc.contributor.researchgroup.spa.fl_str_mv LABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISI
dc.subject.ddc.spa.fl_str_mv 610 - Medicina y salud
topic 610 - Medicina y salud
Cuidados Intensivos
Critical Care
Pediatría
Pediatrics
Signos vitales
modelo oculto de Márkov
cuidado intensivo pediátrico.
inteligencia artificial
Vital signs
Hidden Márkov model
pediatric critical care
Artificial Intelligence
dc.subject.decs.none.fl_str_mv Cuidados Intensivos
Critical Care
Pediatría
Pediatrics
dc.subject.proposal.spa.fl_str_mv Signos vitales
modelo oculto de Márkov
cuidado intensivo pediátrico.
inteligencia artificial
dc.subject.proposal.eng.fl_str_mv Vital signs
Hidden Márkov model
pediatric critical care
Artificial Intelligence
description ilustraciones
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-06-24T20:44:19Z
dc.date.available.none.fl_str_mv 2021-06-24T20:44:19Z
dc.date.issued.none.fl_str_mv 2021-06-19
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/79717
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/79717
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
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spelling Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Niño Vasquez, Luis Fernando529ee5e1893682de94fcec58bfe1f82bIzquierdo Borrero, Ledys Mariaa52b5c84e6feda1bb4c90570435835dcLABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISI2021-06-24T20:44:19Z2021-06-24T20:44:19Z2021-06-19https://repositorio.unal.edu.co/handle/unal/79717Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustracionesResumen En el campo de la monitorización continua de los signos vitales en entornos de cuidados intensivos se ha observado que los signos de alerta temprana "de un deterioro fisiológico inminente” pueden no ser detectados a tiempo, hecho que se agrava no solo por la limitación de los recursos médicos, sino también por el "diluvio de datos" causado por la adquisición de información en pacientes cada vez más complejos durante la atención de rutina. El objetivo de este estudio es desarrollar un modelo probabilístico para predecir los episodios clínicos futuros de un paciente utilizando valores de signos vitales observados antes de un evento clínico. Los signos vitales (por ejemplo, frecuencia cardíaca, presión arterial) se utilizan para controlar las funciones fisiológicas de un paciente y sus cambios simultáneos indican las transiciones entre los estados de salud del paciente. Si tales cambios son anormales, puede conducir a un deterioro fisiológico grave. Se utilizó la metodología CRISP-DM (CRoss-Industry Standard Process for Data Mining) como proceso de minería de datos y luego utilizamos cadenas de Márkov para identificar los estados clínicos por los que pasa el paciente. Después, se aplicó un enfoque basado en un modelo oculto de Márkov (Hidden Márkov Model, HMM) para la clasificación y predicción del deterioro de un paciente calculando la probabilidad de estados clínicos futuros. Ambos modelos de aprendizaje fueron entrenados y evaluados utilizando seis bioseñales de 90 pacientes para un total de 94.678 instancias, recolectadas de una base de datos de pacientes reales que se encontraban en la Unidad de Cuidados Intensivos Pediátricos del Hospital Militar Central de la ciudad de Bogotá, Colombia. La técnica propuesta basada en el seguimiento de múltiples variables fisiológicas mostró resultados prometedores en la identificación precoz del deterioro de los pacientes críticos. (Texto tomado de la fuente)In the field of continuous vital-sign monitoring in critical care settings, it has been observed that the “early warning signs" of impending physiological deterioration can fail to be detected timely and sometimes by resource constrained clinical staff. This effect may be escalated by the “data deluge" caused by acquisition of more complex patient data during routine care. The objective of this study is to develop a probabilistic model for predicting the future clinical episodes of a patient using observed vital sign values prior to a clinical event. Vital signs (e.g., heart rate, blood pressure) are used to monitor a patient's physiological functions and their simultaneous changes indicate transitions between patient's health states. If such changes are abnormal then it may lead to serious physiological deterioration. The CRISP-DM (CRoss-Industry Standard Process for Data Mining) methodology was used as a data mining process and then we used Márkov chains to identify the clinical states through which the patient passes. Then, a Hidden Márkov model (HMM) based approach was applied for classification and prediction of patient's deterioration by computing the probability of future clinical states. Both learning models were trained and evaluated using six vital signs data from 94,678 records from 90 patients, collected from the database of real patients who were in the Pediatric Intensive Care Unit of the Central Military Hospital in the city of Bogota, Colombia. The proposed technique based on monitoring multiple physiological variables showed promising results in early identifying the deterioration of critically ill patients. (Texto tomado de la fuente)Abstract In the field of continuous vital-sign monitoring in critical care settings, it has been observed that the “early warning signs" of impending physiological deterioration can fail to be detected timely and sometimes by resource constrained clinical staff. This effect may be escalated by the “data deluge" caused by acquisition of more complex patient data during routine care. The objective of this study is to develop a probabilistic model for predicting the future clinical episodes of a patient using observed vital sign values prior to a clinical event. Vital signs (e.g., heart rate, blood pressure) are used to monitor a patient's physiological functions and their simultaneous changes indicate transitions between patient's health states. If such changes are abnormal then it may lead to serious physiological deterioration. The CRISP-DM (CRoss-Industry Standard Process for Data Mining) methodology was used as a data mining process and then we used Márkov chains to identify the clinical states through which the patient passes. Then, a Hidden Márkov model (HMM) based approach was applied for classification and prediction of patient's deterioration by computing the probability of future clinical states. Both learning models were trained and evaluated using six vital signs data from 94,678 records from 90 patients, collected from the database of real patients who were in the Pediatric Intensive Care Unit of the Central Military Hospital in the city of Bogota, Colombia. The proposed technique based on monitoring multiple physiological variables showed promising results in early identifying the deterioration of critically ill patients.MaestríaMagíster en Ingeniería BiomédicaEstudio analítico de corte transversal. Se tomaron muestras de monitoria de signos vitales de pacientes atendidos en la UCIP del Hospital Militar Central desde enero de 2018 a enero de 2020, desde 1 mes hasta los 18 meses de edad. Se realizo una descripción de las variables demográficas y clínicas utilizando las medidas más adecuadas de tendencia central y localización según la naturaleza de la variable y su distribución. Se realizo un análisis analítico mediante técnicas basadas en inteligencia computacional, identificando un modelo de aprendizaje automático de análisis, para la descripción de eventos clínicos normales/anormales, que tenga la capacidad de usar las tendencias temporales en datos continuos para la clasificación de eventos clínicos, tomando los datos temporales como una secuencia de cambios de estado clínico, y que se pudiera saber cuál es la probabilidad de que un evento clínico no solo dependa de los valores de signos vitales actuales en el paciente, sino también de una secuencia de mediciones del pasado. Se valido la herramienta computacional empleada a partir del modelo propuesto, adaptando diferentes métricas, para medir sensibilidad, especificidad y precisión, estableciendo las diferencias significativas y estableciendo un nivel de riesgo.Aprendizaje de MáquinasSistemas inteligentes141 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Medicina - Maestría en Ingeniería BiomédicaFacultad de MedicinaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y saludCuidados IntensivosCritical CarePediatríaPediatricsSignos vitalesmodelo oculto de Márkovcuidado intensivo pediátrico.inteligencia artificialVital signsHidden Márkov modelpediatric critical careArtificial IntelligenceModelamiento del espacio de signos vitales para detectar el deterioro de los pacientes en una unidad de cuidados intensivosModeling the vital sign space to detect the deterioration of patients in a pediatric intensive care unitTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM1. 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U.S.: IBM Corporation 1994, [Last Updated: October 2020; cited Nov 6 2020] disponible: https://www.ibm.com/co-es/cloud/machine-learning/pricingGeneralLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/79717/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51ORIGINALModelamiento del espacio de signos vitales ledys izquierdo.pdfModelamiento del espacio de signos vitales ledys izquierdo.pdfTesis Maestría en ingeniería Biomédicaapplication/pdf2356756https://repositorio.unal.edu.co/bitstream/unal/79717/2/Modelamiento%20del%20espacio%20de%20signos%20vitales%20ledys%20izquierdo.pdf8e907b1744dfaf694e201f7127bcdbbbMD5232754316.2021.pdf.modeling the vital sign space.pdf32754316.2021.pdf.modeling the vital sign space.pdfAnexo: 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