Training of machine learning models for recurrence prediction in patients with respiratory pathologies

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UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitleIV Congreso XoveTIC 2021es_ES
UDC.departamentoFisioterapia, Medicina e Ciencias Biomédicases_ES
UDC.grupoInvTecnoloxía Aplicada á Investigación en Ocupación, Igualdade e Saúde (TALIONIS)es_ES
UDC.journalTitleEngineering Proceedingses_ES
UDC.volume7es_ES
dc.contributor.authorMolinero-Rodríguez, Ainhoa
dc.contributor.authorGuerra-Tort, Carla
dc.contributor.authorSuárez-Ulloa, Victoria
dc.contributor.authorLópez Gestal, José Manuel
dc.contributor.authorPereira, Javier
dc.contributor.authorAguiar-Pulido, Vanessa
dc.date.accessioned2021-11-26T10:37:09Z
dc.date.available2021-11-26T10:37:09Z
dc.date.issued2021-10-13
dc.descriptionProceeding paperes_ES
dc.description.abstract[Abstract] Information extracted from electronic health records (EHRs) is used for predictive tasks and clinical pattern recognition. Machine learning techniques also allow the extraction of knowledge from EHR. This study is a continuation of previous work in which EHRs were exploited to make predictions about patients with respiratory diseases. In this study, we will try to predict the recurrence of patients with respiratory diseases using four different machine learning algorithms.es_ES
dc.description.sponsorshipCentro de Investigación de Galicia CITIC and Campus Innova (agreement I+D+ 2019-20) is funded by Consellería de Educación, Universidade e Formación Profesional from Xunta de Galicia and European Union (European Regional Development Fund - FEDER Galicia 2014-2020 Program) by grant ED431G 2019/01 and Universidade da Coruña. Partially supported by the Spanish Ministry of Science (Challenges of Society 2019) PID2019-104323RB-C33
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01
dc.identifier.citationMolinero Rodríguez A, Guerra Tor C, Suárez Ulloa V, López Gestal JM, Pereira J, Aguiar Pulido V. Training of machine learning models for recurrence prediction in patients with respiratory pathologies. Eng Proc. 2021;7(1):20.es_ES
dc.identifier.doi10.3390/engproc2021007020
dc.identifier.issn2673-4591
dc.identifier.urihttp://hdl.handle.net/2183/28957
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104323RB-C33/ES/EVALUACION Y ASESORAMIENTO PARA LA MEJOR EFICIENCIA Y EFECTIVIDAD DE LA TECNOLOGIA DE APOYO/
dc.relation.urihttps://doi.org/10.3390/engproc2021007020es_ES
dc.rightsCreative Commons Attribution 4.0 International License (CC-BY 4.0)es_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectElectronic health record (EHR)es_ES
dc.subjectMachine learninges_ES
dc.subjectLinear discriminant analysises_ES
dc.subjectQuadratic discriminant analysises_ES
dc.subjectK-nearest neighborses_ES
dc.subjectDecision treeses_ES
dc.titleTraining of machine learning models for recurrence prediction in patients with respiratory pathologieses_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationa435b1b6-22a7-49e2-a5bd-854ebe0ac947
relation.isAuthorOfPublication32e6ea1f-7cb0-4c6d-8345-cc8625f08574
relation.isAuthorOfPublication.latestForDiscoverya435b1b6-22a7-49e2-a5bd-854ebe0ac947

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