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

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http://hdl.handle.net/2183/38926
Atribución 4.0 Internacional
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Title
GRALENIA: Antimicrobial Resistance Management based on Natural Language and Artificial Intelligence
Author(s)
Bernardo-Castiñeira, Cristóbal
Bou, Germán
Campos, Manuel
Cánovas-Segura, Bernardo
Figueiras Gómez, Sergio
Gómez-Rodríguez, Carlos
Míguez-Rey, Enrique
Vilares, Jesús
Date
2024
Citation
Bernardo-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 2024
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.
Keywords
Antimicrobial resistance
Artificial intelligence
Cloud services
Deep learning
Healthcare
Industrial research project
Machine learning
Natural language processing
Superbug
 
Description
Included 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.
Editor version
https://ceur-ws.org/Vol-3729/p23_rev.pdf
Rights
Atribución 4.0 Internacional
 
© 2024 Copyright for this paper by its authors.
 
ISSN
1613-0073

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