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Deep learning methods could potentially mitigate domain specific challenges such as limited access to in-domain tools and data sets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-019-0981-y","type":"journal-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T01:02:30Z","timestamp":1577062950000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches"],"prefix":"10.1186","volume":"19","author":[{"given":"Rebecka","family":"Weegar","sequence":"first","affiliation":[]},{"given":"Alicia","family":"P\u00e9rez","sequence":"additional","affiliation":[]},{"given":"Arantza","family":"Casillas","sequence":"additional","affiliation":[]},{"given":"Maite","family":"Oronoz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,23]]},"reference":[{"key":"981_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-78503-5","volume-title":"Clinical Text Mining: Secondary Use of Electronic Patient Records","author":"H Dalianis","year":"2018","unstructured":"Dalianis H. 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