Examinando por Autor "Arias-Ortas, Juan Carlos"
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Ítem Optimising Health Emergency Resource Management from Multi-Model Databases(MDPI, 2022-11-04) Arias-Ortas, Juan Carlos; Cubillas, Juan José; Ramos-Galán, Maria IsabelThe health care sector is one of the most sensitive sectors in our society, and it is believed that the application of specific and detailed database creation and design techniques can improve the quality of patient care. In this sense, better management of emergency resources should be achieved. The development of a methodology to manage and integrate a set of data from multiple sources into a centralised database, which ensures a high quality emergency health service, is a challenge. The high level of interrelation between all of the variables related to patient care will allow one to analyse and make the right strategic decisions about the type of care that will be needed in the future, efficiently managing the resources involved in such care. An optimised database was designed that integrated and related all aspects that directly and indirectly affected the emergency care provided in the province of Jaén (city of Jaén, Andalusia, Spain) over the last eight years. Health, social, economic, environmental, and geographical information related to each of these emergency services was stored and related. Linear and nonlinear regression algorithms were used: support vector machine (SVM) with linear kernel and generated linear model (GLM), and the nonlinear SVM with Gaussian kernel. Predictive models of emergency demand were generated with a success rate of over 90%.Ítem Predicción de patologías más comunes y emergencias sanitarias basada en variables espaciales mediante el uso de la minería de datos(2024-07-23) Arias-Ortas, Juan Carlos; Ramos-Galán, Maria Isabel; Cubillas, Juan José; Universidad de Jaén. Departamento de InformáticaEl sector sanitario es uno de los más sensibles de nuestra sociedad. La atención oportuna en el sector sanitario es esencial para la recuperación de los pacientes, y más aún en caso de emergencia sanitaria. En estos casos, es esencial una gestión adecuada de los recursos humanos y técnicos, que son limitados y deben movilizarse de forma óptima y eficaz. Se considera que la aplicación de técnicas específicas y detalladas de creación y diseño de bases de datos puede mejorar la calidad de la atención a los pacientes. The health sector is one of the most sensitive sectors in our society. Timely care in the health sector is essential for the recovery of patients, and even more so in the event of a health emergency. In these cases, appropriate management of human and technical resources, which are limited and need to be mobilised in an optimal and efficient way, is essential. It is considered that the application of specific and detailed database creation and design techniques can improve the quality of patient care.Ítem Predicting emergency health care demands due to respiratory diseases(Elsevier, 2023-07-28) Arias-Ortas, Juan Carlos; Ramos-Galán, Maria Isabel; Cubillas, Juan JoséBackground: Timely care in the health sector is essential for the recovery of patients, and even more so in the case of a health emergency. In these cases, appropriate management of human and technical resources is essential. These are limited and must be mobilised in an optimal and efficient manner. Objective: This paper analyses the use of the health emergency service in a city, Jaén, in the south of Spain. The study is focused on the most recurrent case in this service, respiratory diseases. Methods: Machine Learning algorithms are used in which the input variables are multisource data and the target attribute is the prediction of the number of health emergency demands that will occur for a selected date. Health, social, economic, environmental, and geospatial data related to each of the emergency demands were integrated and related. Linear and nonlinear regression algorithms were used: support vector machine (SVM) with linear kernel and generated linear model (GLM), and the nonlinear SVM with Gaussian kernel. Results: Predictive models of emergency demand due to respiratory disseases were generated with am absolute error better than 35 %. Conclusions: This model helps to make decisions on the efficient sizing of emergency health resources to manage and respond in the shortest possible time to patients with respiratory diseases requiring urgent care in the city of Jaén.