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

Autor: Mercedes Arguello-Casteleiro, Robert Stevens, Julio Des-Diz, Chris Wroe, Maria Jesus Fernandez-Prieto, Nava Maroto, Diego Maseda-Fernandez, George Demetriou, Simon Peters, Peter-John M. Noble, Phil H. Jones, Jo Dukes-McEwan, Alan D. Radford, John Keane, Goran Nenadic

Publikováno v: Journal of Biomedical Semantics, Vol 10, Iss S1, Pp 1-28 (2019)

Abstract Background Deep Learning opens up opportunities for routinely scanning large bodies of biomedical literature and clinical narratives to represent the meaning of biomedical and clinical terms. However, the validation and integration of this k

Externí odkaz: https://doaj.org/article/032fc0428bee42aebfecfd98fd215865

Semantic Deep Learning: Prior Knowledge and a Type of Four-Term Embedding Analogy to Acquire Treatments for Well-Known Diseases

Autor: Nava Maroto, Maria Jesus Fernandez Prieto, Julio Des Diz, Mercedes Arguello Casteleiro, Simon Peters, Chris Wroe, Robert Stevens, Diego Maseda Fernandez, Carlos Sevillano Torrado

Publikováno v: JMIR Medical Informatics, Vol 8, Iss 8, p e16948 (2020)
Arguello Casteleiro, M, Des-Diz, J, Maroto, N, Fernandez-Prieto, M J, Peters, S, Wroe, C, Torrado, C S, Fernandez, D M & Stevens, R 2020, ' Semantic Deep Learning: prior knowledge and a type of four-term embedding analogies to acquire treatments for wellknown diseases ', JMIR medical informatics, vol. 8, no. 8 . https://doi.org/10.2196/16948
JMIR Medical Informatics

Background How to treat a disease remains to be the most common type of clinical question. Obtaining evidence-based answers from biomedical literature is difficult. Analogical reasoning with embeddings from deep learning (embedding analogies) may ext

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00967d3c94a09b2d1b5b37703bc406d9
https://medinform.jmir.org/2020/8/e16948

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

Autor: George Demetriou, Mercedes Arguello-Casteleiro, Phil H. Jones, Chris Wroe, John A. Keane, M.J. Fernandez-Prieto, Julio Des-Diz, Peter-John M. Noble, Simon Peters, Nava Maroto, Goran Nenadic, Jo Dukes-McEwan, Alan D Radford, Diego Maseda-Fernandez, Robert Stevens

Publikováno v: Journal of Biomedical Semantics
Arguello Casteleiro, M, Stevens, R, Des-Diz, J, Wroe, C, Fernandez-Prieto, M J, Maroto, N, Maseda Fernandez, D, Demetriou, G, Peters, S, Noble, P-J M, Jones, P H, Dukes-McEwan, J, Radford, A D, Keane, J & Nenadic, G 2019, ' Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes ', Journal of Biomedical Semantics, vol. 10, no. 22 . https://doi.org/10.1186/s13326-019-0212-6
Journal of Biomedical Semantics, Vol 10, Iss S1, Pp 1-28 (2019)

Background Deep Learning opens up opportunities for routinely scanning large bodies of biomedical literature and clinical narratives to represent the meaning of biomedical and clinical terms. However, the validation and integration of this knowledge

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fef8eb00a705cc0b53d328d1c649a6b
https://doi.org/10.1186/s13326-019-0212-6

A Case Study on Sepsis Using PubMed and Deep Learning for Ontology Learning

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

Publikováno v: Studies in health technology and informatics. 235

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::86980d09e4f9823f0fb27af09693e396
https://pubmed.ncbi.nlm.nih.gov/28423846