Escalante H.J.Montes-y-Gomez M.Segura A.de Dios Murillo J.2020-03-262020-03-262016Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10022 LNAI, pp. 212-221978331947954503029743https://hdl.handle.net/20.500.12585/8997High demand periods and under-staffing due to financial constraints cause Emergency Departments (EDs) to frequently exhibit over-crowding and slow response times to provide adequate patient care. In response, Lean Thinking has been applied to help alleviate some of these issues and improve patient handling, with success. Lean approaches in EDs include separate patient streams, with low-complexity patients treated in a so-called Fast Track, in order to reduce total waiting time and to free-up capacity to treat more complicated patients in a timely manner. In this work we propose the use of Machine Learning techniques in a Lean Pediatric ED to correctly predict which patients should be admitted to the Fast Track, given their signs and symptoms. Charts from 1205 patients of the emergency department of Hospital Napoleón Franco Pareja in Cartagena - Colombia, were used to construct a dataset and build several predictive models. Validation and test results are promising and support the validity of this approach and further research on the subject. © Springer International Publishing AG 2016.Recurso electrónicoapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/conferenceObject10.1007/978-3-319-47955-2_18Emergency departmentFast trackLeanLogistic regressionMachine learningNeural networksPCASVMTriageArtificial intelligenceComplex networksEmergency roomsHospitalsNeural networksPatient monitoringPatient treatmentPediatricsEmergency departmentsFast tracksLeanLogistic regressionsTriageLearning systemsinfo:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 InternacionalUniversidad Tecnológica de BolívarRepositorio UTB557824265005719183971955782490400