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dc.contributor.authorGuerra Tort, Carla
dc.contributor.authorAguiar-Pulido, Vanessa
dc.contributor.authorSuárez-Ulloa, Victoria
dc.contributor.authorDocampo Boedo, Francisco
dc.contributor.authorLópez Gestal, José Manuel
dc.contributor.authorPereira-Loureiro, Javier
dc.date.accessioned2021-01-15T13:16:44Z
dc.date.available2021-01-15T13:16:44Z
dc.date.issued2020-09-09
dc.identifier.citationGuerra Tort, C.; Aguiar Pulido, V.; Suárez Ulloa, V.; Docampo Boedo, F.; López Gestal, J. M.; Pereira Loureiro, J. Electronic Health Records Exploitation Using Artificial Intelligence Techniques. Proceedings 2020, 54, 60. https://doi.org/10.3390/proceedings2020054060es_ES
dc.identifier.issn2504-3900
dc.identifier.urihttp://hdl.handle.net/2183/27154
dc.description.abstract[Abstract] The exploitation of electronic health records (EHRs) has multiple utilities, from predictive tasks and clinical decision support to pattern recognition. Artificial Intelligence (AI) allows to extract knowledge from EHR data in a practical way. In this study, we aim to construct a Machine Learning model from EHR data to make predictions about patients. Specifically, we will focus our analysis on patients suffering from respiratory problems. Then, we will try to predict whether those patients will have a relapse in less than 6, 12 or 18 months. The main objective is to identify the characteristics that seem to increase the relapse risk. At the same time, we propose an exploratory analysis in search of hidden patterns among data. These patterns will help us to classify patients according to their specific conditions for some clinical variables.es_ES
dc.description.sponsorshipCentro de Investigación de Galicia CITIC is funded by Consellería de Educación, Universidades e Formación Profesional from Xunta de Galicia and European Union (European Regional Development Fund—FEDER Galicia 2014-2020 Program) by grant ED431G 2019/01. Partially supported by the Spanish Ministry of Science (Challenges of Society 2019) PID2019-104323RB-C33es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo: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/proceedings2020054060es_ES
dc.rightsAtribución 4.0 Internacionales_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectElectronic health record (EHR)es_ES
dc.subjectArtificial intelligence (AI)es_ES
dc.subjectRelapsees_ES
dc.subjectRespiratory diseaseses_ES
dc.titleElectronic Health Records Exploitation Using Artificial Intelligence Techniqueses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleProceedingses_ES
UDC.volume54es_ES
UDC.issue1es_ES
UDC.startPage60es_ES
UDC.endPage62es_ES
UDC.conferenceTitle3rd XoveTIC Conference; A Coruña, Spain; 8–9 October 2020es_ES


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