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Electronic Health Records Exploitation Using Artificial Intelligence Techniques
dc.contributor.author | Guerra Tort, Carla | |
dc.contributor.author | Aguiar-Pulido, Vanessa | |
dc.contributor.author | Suárez-Ulloa, Victoria | |
dc.contributor.author | Docampo Boedo, Francisco | |
dc.contributor.author | López Gestal, José Manuel | |
dc.contributor.author | Pereira-Loureiro, Javier | |
dc.date.accessioned | 2021-01-15T13:16:44Z | |
dc.date.available | 2021-01-15T13:16:44Z | |
dc.date.issued | 2020-09-09 | |
dc.identifier.citation | Guerra 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/proceedings2020054060 | es_ES |
dc.identifier.issn | 2504-3900 | |
dc.identifier.uri | http://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.sponsorship | Centro 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-C33 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info: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.uri | https://doi.org/10.3390/proceedings2020054060 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Electronic health record (EHR) | es_ES |
dc.subject | Artificial intelligence (AI) | es_ES |
dc.subject | Relapse | es_ES |
dc.subject | Respiratory diseases | es_ES |
dc.title | Electronic Health Records Exploitation Using Artificial Intelligence Techniques | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Proceedings | es_ES |
UDC.volume | 54 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | 60 | es_ES |
UDC.endPage | 62 | es_ES |
UDC.conferenceTitle | 3rd XoveTIC Conference; A Coruña, Spain; 8–9 October 2020 | es_ES |