Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes.

Autor: Arguello-Casteleiro M; School of Computer Science, University of Manchester, Manchester, UK. m.arguello@manchester.ac.uk., Stevens R; School of Computer Science, University of Manchester, Manchester, UK., Des-Diz J; Hospital do Salnés, Villagarcía de Arousa, Pontevedra, Spain., Wroe C; BMJ, Tavistock Square, London, UK., Fernandez-Prieto MJ; Salford Languages, University of Salford, Salford, UK., Maroto N; Departamento de Lingüística Aplicada a la Ciencia y a la Tecnología, Universidad Politécnica de Madrid, Madrid, Spain., Maseda-Fernandez D; Midcheshire Hospital Foundation Trust, NHS England, Crewe, UK.; School of Medical Sciences, University of Manchester, Manchester, UK., Demetriou G; School of Computer Science, University of Manchester, Manchester, UK., Peters S; School of Social Sciences, University of Manchester, Manchester, UK., Noble PM; Small Animal Veterinary Surveillance Network, University of Liverpool, Liverpool, UK., Jones PH; Small Animal Veterinary Surveillance Network, University of Liverpool, Liverpool, UK., Dukes-McEwan J; Small Animal Teaching Hospital, University of Liverpool, Liverpool, UK., Radford AD; Small Animal Veterinary Surveillance Network, University of Liverpool, Liverpool, UK., Keane J; School of Computer Science, University of Manchester, Manchester, UK.; Manchester Institute of Biotechnology, University of Manchester, Manchester, UK., Nenadic G; School of Computer Science, University of Manchester, Manchester, UK.; Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.; Health eResearch Centre, University of Manchester, Manchester, UK.

Publikováno v: Journal of biomedical semantics [J Biomed Semantics] 2019 Nov 12; Vol. 10 (Suppl 1), pp. 22. Date of Electronic Publication: 2019 Nov 12.

Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature.

Autor: Arguello Casteleiro M; School of Computer Science, University of Manchester, Manchester, UK., Demetriou G; School of Computer Science, University of Manchester, Manchester, UK., Read W; School of Computer Science, University of Manchester, Manchester, UK., Fernandez Prieto MJ; Salford Languages, University of Salford, Salford, UK., Maroto N; Departamento de Lingüística Aplicada a la Ciencia y a la Tecnología, Universidad Politécnica de Madrid, Madrid, Spain., Maseda Fernandez D; Midcheshire Hospital Foundation Trust NHS, Crewe, England, UK., Nenadic G; School of Computer Science, University of Manchester, Manchester, UK.; Manchester Institute of Biotechnology, University of Manchester, Manchester, UK., Klein J; Institut National de la Santé et de la Recherche Medicale (INSERM) U1048, Toulouse, France.; Universite Toulouse III Paul Sabatier, route de Narbonne, Toulouse, France., Keane J; School of Computer Science, University of Manchester, Manchester, UK.; Manchester Institute of Biotechnology, University of Manchester, Manchester, UK., Stevens R; School of Computer Science, University of Manchester, Manchester, UK. Robert.Stevens@manchester.ac.uk.

Publikováno v: Journal of biomedical semantics [J Biomed Semantics] 2018 Apr 12; Vol. 9 (1), pp. 13. Date of Electronic Publication: 2018 Apr 12.

A case study on sepsis using PubMed and Deep Learning for ontology learning

Autor: Arguello Casteleiro, Mercedes, Maseda Fernandez, Diego, Demetriou, George, Read, Warren, Fernandez-Prieto, MJ, Des Diz, Julio, Nenadic, Goran, Keane, John, Stevens, Robert

We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora.\ud Term extraction is used as the first step of an ontology learning process that aims to (semi-)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=core_ac_uk__::0da450288dc058fd31599cf6516ecc30

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Publikováno v: Yearbook of Medical Informatics; 2019, Vol. 28 Issue 1, p156-157, 2p

Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes: Findings from the 2019 IMIA Yearbook Section on Knowledge Representation and Management.

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