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dc.contributor.authorMarmolejo-Saucedo, José-Antonio
dc.contributor.otherCampus Ciudad de México
dc.identifier.citationRodríguez Aguilar, R., Marmolejo Saucedo, J. A. y Vasant, P. (2019). Machine Learning Applied to the Measurement of Quality in Health Services in Mexico: The Case of the Social Protection in Health System. En: Vasant, P., Zelinka, I. y Weber, G.-W. (editores), Intelligent Computing & Optimization, (Advances in Intelligent Systems and Computing, vol. 866), (pp. 560-572). Cham : Springer International. DOI: 10.1007/978-3-030-00979-3_59es_ES, en_US
dc.identifier.isbn9783030009786es_ES, en_US
dc.identifier.issn2194-5357es_ES, en_US
dc.description.abstractTo propose a satisfaction indicator of users of health services affiliated to the Social Protection System in Health (SPSS). Identify the effect of the main factors that are directly related to the satisfaction level and perception of quality of health services. A machine-learning model based on Logistic Models and Principal Components was developed to estimate a satisfaction index. The survey data collected for the “SPSS 2014 User’s Satisfaction Study” was used, considering a sample of 28,290 users. The proposed model shows, in general, the positive perception of quality of health services (national average 0.0756). There are factors statistically significant that influence these results, the good perception of infrastructure (OR:2.12; CI 95%:1.9–2.36); the gratuity of the service provided (OR:1.98; CI 95%: 1.42–2.76); and full medicines supply (OR:1.81; CI 95%:1.91–2.36). The proposed index can be used as an indicator for improving health care quality of the population covered by the SPSS. © 2019, Springer Nature Switzerland AG.es_ES, en_US
dc.publisherSpringer Verlages_ES, en_US
dc.relationVersión del editores_ES, en_US
dc.relation.ispartofREPOSITORIO SCRIPTAes_ES, en_US
dc.relation.ispartofOPENAIREes_ES, en_US
dc.relation.ispartofseriesAdvances in Intelligent Systems and Computing
dc.rightsAcceso Cerradoes_ES, en_US
dc.rights.uri, en_US
dc.sourceIntelligent Computing & Optimization
dc.subjectCare qualityes_ES, en_US
dc.subjectHealth surveyses_ES, en_US
dc.subjectLogistic modelses_ES, en_US
dc.subjectMachine learninges_ES, en_US
dc.subjectPrincipal componentses_ES, en_US
dc.subjectQuality indicatorses_ES, en_US
dc.subjectSatisfactiones_ES, en_US
dc.subjectIntelligent computinges_ES, en_US
dc.subjectMachine componentses_ES, en_US
dc.subjectSurveyses_ES, en_US
dc.subjectHealth surveyses_ES, en_US
dc.subjectLogistic modelses_ES, en_US
dc.subjectPrincipal Componentses_ES, en_US
dc.subjectLearning systemses_ES, en_US
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes_ES, en_US
dc.subject.classificationMEDICINA Y CIENCIAS DE LA SALUD
dc.subject.classificationCiencias de la Salud
dc.titleMachine learning applied to the measurement of quality in health services in Mexico : the case of the social protection in health systemes_ES, en_US
dc.typeContribución a congresoes_ES, en_US
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