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dc.contributor.editorEscalante H.J.
dc.contributor.editorMontes-y-Gomez M.
dc.contributor.editorSegura A.
dc.contributor.editorde Dios Murillo J.
dc.creatorCaicedo-Torres W.
dc.creatorPaternina Á.
dc.creatorPinzón H.
dc.date.accessioned2020-03-26T16:32:44Z
dc.date.available2020-03-26T16:32:44Z
dc.date.issued2016
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10022 LNAI, pp. 247-258
dc.identifier.isbn9783319479545
dc.identifier.issn03029743
dc.identifier.urihttps://hdl.handle.net/20.500.12585/8998
dc.description.abstractInfection by dengue-virus is prevalent and a public health issue in tropical countries worldwide. Also, in developing nations, child populations remain at risk of adverse events following an infection by dengue virus, as the necessary care is not always accessible, or health professionals are without means to cheaply and reliably predict how likely is for a patient to experience severe Dengue. Here, we propose a classification model based on Machine Learning techniques, which predicts whether or not a pediatric patient will be admitted into the pediatric Intensive Care Unit, as a proxy for Dengue severity. Different Machine Learning techniques were trained and validated using Stratified 5-Fold Cross-Validation, and the best model was evaluated on a disjoint test set. Cross-Validation results showed an SVM with Gaussian Kernel outperformed the other models considered, with an 0.81 Receiver Operating Characteristic Area Under the Curve (ROC AUC) score. Subsequent results over the test set showed a 0.75 ROC AUC score. Validation and test results are promising and support further research and development. © Springer International Publishing AG 2016.
dc.format.mediumRecurso electrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer Verlag
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84994130153&doi=10.1007%2f978-3-319-47955-2_21&partnerID=40&md5=65a9cbf885b81a5735f5593296b0d244
dc.titleMachine learning models for early dengue severity prediction
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datacite.rightshttp://purl.org/coar/access_right/c_16ec
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94f
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.source.event15th Ibero-American Conference on Advances in Artificial Intelligence, IBERAMIA 2016
dc.type.driverinfo:eu-repo/semantics/conferenceObject
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1007/978-3-319-47955-2_21
dc.subject.keywordsChildren
dc.subject.keywordsDengue
dc.subject.keywordsLogistic regression
dc.subject.keywordsMachine learning
dc.subject.keywordsNaive bayes
dc.subject.keywordsPICU
dc.subject.keywordsSeverity
dc.subject.keywordsSVM
dc.subject.keywordsArtificial intelligence
dc.subject.keywordsHealth risks
dc.subject.keywordsIntensive care units
dc.subject.keywordsLearning algorithms
dc.subject.keywordsPediatrics
dc.subject.keywordsViruses
dc.subject.keywordsChildren
dc.subject.keywordsDengue
dc.subject.keywordsLogistic regressions
dc.subject.keywordsNaive bayes
dc.subject.keywordsPICU
dc.subject.keywordsSeverity
dc.subject.keywordsLearning systems
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.ccAtribución-NoComercial 4.0 Internacional
dc.identifier.instnameUniversidad Tecnológica de Bolívar
dc.identifier.reponameRepositorio UTB
dc.relation.conferencedate23 November 2016 through 25 November 2016
dc.type.spaConferencia
dc.identifier.orcid55782426500
dc.identifier.orcid57203489700
dc.identifier.orcid55782490400


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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.