Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches
dc.contributor.author | Weegar, Rebecka | |
dc.contributor.author | Pérez Ramírez, Alicia | |
dc.contributor.author | Casillas Rubio, Arantza | |
dc.contributor.author | Oronoz Anchordoqui, Maite | |
dc.date.accessioned | 2020-03-26T09:43:20Z | |
dc.date.available | 2020-03-26T09:43:20Z | |
dc.date.issued | 2019-12-23 | |
dc.identifier.citation | BMC Medical Informatics and Decision Making 19(7) : (2019) // Article ID 274 | es_ES |
dc.identifier.issn | 1472-6947 | |
dc.identifier.uri | http://hdl.handle.net/10810/42355 | |
dc.description.abstract | Background Text mining and natural language processing of clinical text, such as notes from electronic health records, requires specific consideration of the specialized characteristics of these texts. Deep learning methods could potentially mitigate domain specific challenges such as limited access to in-domain tools and data sets. Methods A bi-directional Long Short-Term Memory network is applied to clinical notes in Spanish and Swedish for the task of medical named entity recognition. Several types of embeddings, both generated from in-domain and out-of-domain text corpora, and a number of generation and combination strategies for embeddings have been evaluated in order to investigate different input representations and the influence of domain on the final results. Results For Spanish, a micro averaged F1-score of 75.25 was obtained and for Swedish, the corresponding score was 76.04. The best results for both languages were achieved using embeddings generated from in-domain corpora extracted from electronic health records, but embeddings generated from related domains were also found to be beneficial. Conclusions A recurrent neural network with in-domain embeddings improved the medical named entity recognition compared to shallow learning methods, showing this combination to be suitable for entity recognition in clinical text for both languages. | es_ES |
dc.description.sponsorship | The publication cost of this article was funded by Stockholm University Library | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | BMC | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | clinical text mining | es_ES |
dc.subject | unstructured electronic health records | es_ES |
dc.subject | medical named entity recognition | es_ES |
dc.subject | recurrent neural network corpus | es_ES |
dc.title | Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-0981-y | es_ES |
dc.identifier.doi | 10.1186/s12911-019-0981-y | |
dc.departamentoes | Electricidad y electrónica | es_ES |
dc.departamentoes | Lenguajes y sistemas informáticos | es_ES |
dc.departamentoeu | Elektrizitatea eta elektronika | es_ES |
dc.departamentoeu | Lengoaia eta Sistema Informatikoak |
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Except where otherwise noted, this item's license is described as The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.