dc.creatorArias, J.C.
dc.creatorCubillas, Juan Jose
dc.creatorRamos, Maria I.
dc.date.accessioned2023-05-08T08:22:45Z
dc.date.accessioned2023-09-07T15:19:27Z
dc.date.available2023-05-08T08:22:45Z
dc.date.available2023-09-07T15:19:27Z
dc.date.created2023-05-08T08:22:45Z
dc.identifierArias, J. C., Cubillas, J. J., & Ramos, M. I. (2022). Optimising Health Emergency Resource Management from Multi-Model Databases. Electronics, 11(21), 3602. MDPI AG. Retrieved from http://dx.doi.org/10.3390/electronics11213602
dc.identifier2079-9292
dc.identifierhttps://reunir.unir.net/handle/123456789/14620
dc.identifierhttps://doi.org/10.3390/electronics11213602
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8731946
dc.description.abstractThe 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%.
dc.languageeng
dc.publisherElectronics (Switzerland)
dc.relation;vol. 11, nº 21
dc.relationhttps://www.mdpi.com/2079-9292/11/21/3602
dc.rightsopenAccess
dc.subjectdatabase design
dc.subjectgeospatial data
dc.subjecthealthcare
dc.subjectScopus
dc.subjectJCR
dc.titleOptimising Health Emergency Resource Management from Multi-Model Databases
dc.typeArticulo Revista Indexada


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