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PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts

Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step fo...

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Autores principales: Armengol-Estapé, Jordi, Soares, Felipe, Marimon, Montserrat, Krallinger, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korea Genome Organization 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6808625/
https://www.ncbi.nlm.nih.gov/pubmed/31307130
http://dx.doi.org/10.5808/GI.2019.17.2.e15
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author Armengol-Estapé, Jordi
Soares, Felipe
Marimon, Montserrat
Krallinger, Martin
author_facet Armengol-Estapé, Jordi
Soares, Felipe
Marimon, Montserrat
Krallinger, Martin
author_sort Armengol-Estapé, Jordi
collection PubMed
description Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL).
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spelling pubmed-68086252019-10-30 PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts Armengol-Estapé, Jordi Soares, Felipe Marimon, Montserrat Krallinger, Martin Genomics Inform Application Note Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL). Korea Genome Organization 2019-06-19 /pmc/articles/PMC6808625/ /pubmed/31307130 http://dx.doi.org/10.5808/GI.2019.17.2.e15 Text en (c) 2019, Korea Genome Organization (CC) This is an open-access article distributed under the terms of the Creative Commons Attribution license(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Application Note
Armengol-Estapé, Jordi
Soares, Felipe
Marimon, Montserrat
Krallinger, Martin
PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts
title PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts
title_full PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts
title_fullStr PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts
title_full_unstemmed PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts
title_short PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts
title_sort pharmaconer tagger: a deep learning-based tool for automatically finding chemicals and drugs in spanish medical texts
topic Application Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6808625/
https://www.ncbi.nlm.nih.gov/pubmed/31307130
http://dx.doi.org/10.5808/GI.2019.17.2.e15
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