GRALENIA: Antimicrobial Resistance Management based on Natural Language and Artificial Intelligence

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UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitleSEPLN-CEDI-PD 2024 - Seminar of the Spanish Society for Natural Language Processing: Projects and System Demonstrations, co-located with the 7th Spanish Conference on Informatics (CEDI 2024)es_ES
UDC.departamentoLetrases_ES
UDC.endPage105es_ES
UDC.grupoInvLingua e Sociedade da Información (LYS)es_ES
UDC.journalTitleCEUR Workshop Proceedingses_ES
UDC.startPage100es_ES
UDC.volume3729es_ES
dc.contributor.authorBernardo-Castiñeira, Cristóbal
dc.contributor.authorBou, Germán
dc.contributor.authorCampos, Manuel
dc.contributor.authorCánovas-Segura, Bernardo
dc.contributor.authorFigueiras Gómez, Sergio
dc.contributor.authorGómez-Rodríguez, Carlos
dc.contributor.authorMíguez-Rey, Enrique
dc.contributor.authorVilares, Jesús
dc.date.accessioned2024-09-09T15:16:27Z
dc.date.available2024-09-09T15:16:27Z
dc.date.issued2024
dc.descriptionIncluded in Proceedings of the Seminar of the Spanish Society for Natural Language Processing: Projects and System Demonstrations (SEPLN-CEDI-PD 2024) co-located with the 7th Spanish Conference on Informatics (CEDI 2024) A Coruña, Spain, June 19-20, 2024.es_ES
dc.description.abstract[Abstract]: The objective of GRALENIA project is to develop a multidisciplinary, comprehensive and interoperable platform incorporating artificial intelligence algorithms and natural language processing techniques to improve the management of antimicrobial resistance (AMR) and reduce the impact of antimicrobial- or antibiotic-resistant microorganisms (aka superbugs) in hospitals. GRALENIA is supported through a Red.es grant for research and development projects in artificial intelligence and other digital technologies and their integration into value chains.es_ES
dc.description.sponsorshipThis work is partially funded by the GRALENIA project (reference 2021/C005/00150055) supported by the Spanish Ministry of Economic Affairs and Digital Transformation, the Spanish Secretariat of State for Digitization and Artificial Intelligence, Red.es and by the NextGenerationEU funding. Research on CITIC is also financed by the Xunta de Galicia through the collaboration agreement between the Ministry of Culture, Education, Vocational Training and Universities and the Galician universities to reinforce the System's research centers University of Galicia (CIGUS) We would also like to thank Dr. José María Gutiérrez Urbón (CHUAC Pharmacy service), Dr. Laura Gutiérrez Fernández (CHUAC Infectious Diseases unit), Daniel Llamas Gómez (current Chief Information Officer-CIO, A Coruña-Cee Health District, SERGAS) and Guillermo Vázquez González (former CIO) for their valuable contributions to the project.es_ES
dc.identifier.citationBernardo-Castiñeira, C., Bou, G., Campos, M., Cánovas-Segura, B., Gómez, S. F., Gómez-Rodríguez, C., ... & Vilares, J. (2023). GRALENIA: Antimicrobial Resistance Management based on Natural Language and Artificial Intelligence. CEUR Workshop Proceedings. Vol. 3729, Pages 100 – 105. Seminar of the Spanish Society for Natural Language Processing: Projects and System Demonstrations, SEPLN-CEDI-PD 2024, A Coruna, June 2024es_ES
dc.identifier.issn1613-0073
dc.identifier.urihttp://hdl.handle.net/2183/38926
dc.language.isoenges_ES
dc.publisherCEUR-WSes_ES
dc.relation.urihttps://ceur-ws.org/Vol-3729/p23_rev.pdfes_ES
dc.rightsAtribución 4.0 Internacionales_ES
dc.rights© 2024 Copyright for this paper by its authors.es_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectAntimicrobial resistancees_ES
dc.subjectArtificial intelligencees_ES
dc.subjectCloud serviceses_ES
dc.subjectDeep learninges_ES
dc.subjectHealthcarees_ES
dc.subjectIndustrial research projectes_ES
dc.subjectMachine learninges_ES
dc.subjectNatural language processinges_ES
dc.subjectSuperbuges_ES
dc.titleGRALENIA: Antimicrobial Resistance Management based on Natural Language and Artificial Intelligencees_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication909e08d1-6ed1-4b99-9e9e-c64eb72e7dea
relation.isAuthorOfPublicatione70a3969-39f6-4458-9339-3b71756fa56e
relation.isAuthorOfPublication3313b723-2288-4d9d-b0e7-32732c9c78d5
relation.isAuthorOfPublication.latestForDiscovery909e08d1-6ed1-4b99-9e9e-c64eb72e7dea

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