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Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches

Rebecka Weegar, Alicia Pérez, Arantza Casillas, Maite Oronoz
<span title="2019-12-23">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bnylrk2y7bfnrn7u2f2vjkx7ta" style="color: black;">BMC Medical Informatics and Decision Making</a> </i> &nbsp;
Deep learning methods could potentially mitigate domain specific challenges such as limited access to in-domain tools and data sets.  ...  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.  ...  of Excellence in Health-Related e-Sciences (NIASC); financed by NordForsk (Project number 62721).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12911-019-0981-y">doi:10.1186/s12911-019-0981-y</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31865900">pmid:31865900</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6927099/">pmcid:PMC6927099</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e4msmcmgzjg7vjxmjv37l4hpfa">fatcat:e4msmcmgzjg7vjxmjv37l4hpfa</a> </span>
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Analyzing transfer learning impact in biomedical cross-lingual named entity recognition and normalization

Renzo M. Rivera-Zavala, Paloma Martínez
<span title="2021-12-17">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
Named Entity Recognition (NER) is the first step for information and knowledge acquisition when we deal with unstructured texts.  ...  Conclusion These results prove that deep learning models with in-domain knowledge learned from large-scale datasets highly improve named entity recognition performance.  ...  About This Supplement This article has been published as part of BMC Bioinformatics Volume 22, Supplement 1 2021: Recent Progresses with BioNLP Open Shared Tasks-Part 2.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-021-04247-9">doi:10.1186/s12859-021-04247-9</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34920703">pmid:34920703</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8680060/">pmcid:PMC8680060</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dsavlikunvc4zeejhsl74hteo4">fatcat:dsavlikunvc4zeejhsl74hteo4</a> </span>
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Combining word embeddings to extract chemical and drug entities in biomedical literature

Pilar López-Úbeda, Manuel Carlos Díaz-Galiano, L. Alfonso Ureña-López, M. Teresa Martín-Valdivia
<span title="2021-12-17">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
Methods In this paper we evaluate two important tasks in NLP: the named entity recognition (NER) and Entity indexing using the SNOMED-CT terminology.  ...  Results For the NER task we present a neural network composed of BiLSTM with a CRF sequential layer where different word embeddings are combined as an input to the architecture.  ...  About this supplement This article has been published as part of BMC Bioinformatics Volume 22, Supplement 1 2021: Recent Progresses with BioNLP Open Shared Tasks -Part 2.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-021-04188-3">doi:10.1186/s12859-021-04188-3</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34920708">pmid:34920708</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8684055/">pmcid:PMC8684055</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iwdqlvquyfhwvbfpz4chqpetym">fatcat:iwdqlvquyfhwvbfpz4chqpetym</a> </span>
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Deep learning with language models improves named entity recognition for PharmaCoNER

Cong Sun, Zhihao Yang, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang
<span title="2021-12-17">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
PharmaCoNER is a named entity recognition challenge to recognize pharmacological entities from Spanish texts.  ...  Although considerable efforts have been made to recognize biomedical entities in English texts, to date, only few limited attempts were made to recognize them from biomedical texts in other languages.  ...  Acknowledgements The authors want to thank the anonymous reviewers for their helpful comments and suggestions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-021-04260-y">doi:10.1186/s12859-021-04260-y</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34920700">pmid:34920700</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8684061/">pmcid:PMC8684061</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fnnm33v3sjd2llk6ybttpzgzsi">fatcat:fnnm33v3sjd2llk6ybttpzgzsi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220116124822/https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-021-04260-y.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/75/28/7528931e452eeb13d4a06629747a3ca4a58c7ab0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-021-04260-y"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8684061" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish [article]

Manuel Stoeckel, Wahed Hemati, Alexander Mehler
<span title="2019-10-08">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We train a state-of-the-art BiLSTM-CRF sequence tagger with stacked Pooled Contextualized Embeddings, word and sub-word embeddings using the open-source framework FLAIR.  ...  In this paper, we describe an approach to Task 1 of the PharmaCoNER Challenge, which involves the recognition of mentions of chemicals and drugs in Spanish medical texts.  ...  Acknowledgements We would like to thank the anonymous reviewers for their fair opinions and the organisers for their patience and help with problems during the submission of the workshop papers.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.03387v1">arXiv:1910.03387v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6gswfrnmu5aufplfvfhohy47ki">fatcat:6gswfrnmu5aufplfvfhohy47ki</a> </span>
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Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives

Liliya Akhtyamova, Paloma Martinez, Karin Verspoor, John Cardiff
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
We are also thankful to Leonardo Campillos who gratefully helped us prepare the analysis on Spanish clinical texts' peculiarities.  ...  ACKNOWLEDGMENT We would like to thank anonymous reviewers for their invaluable feedback.  ...  PHARMACONER 2019 SHARED TASK PharmacoNER is "the first task on chemical and drug mention recognition from Spanish medical texts, namely from a corpus of Spanish clinical case studies" [1] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3018688">doi:10.1109/access.2020.3018688</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u5veccj2ovebdh32obx3mcegtu">fatcat:u5veccj2ovebdh32obx3mcegtu</a> </span>
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Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes

Oswaldo Solarte Pabón, Maria Torrente, Mariano Provencio, Alejandro Rodríguez-Gonzalez, Ernestina Menasalvas
<span title="2021-01-19">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
Results obtained show an F-score of 90% in the named entity recognition task, and a 89% F-score in the task of relating the cancer diagnosis to the diagnosis date.  ...  Despite efforts to develop models for extracting medical concepts from clinical notes, there are still some challenges in particular to be able to relate concepts to dates.  ...  Lung Cancer Named Entity Recognition In this section, a deep learning model to extract lung cancer named entities from clinical notes written in Spanish is described.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app11020865">doi:10.3390/app11020865</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/odpnldls7jhetgh23zvss5lm6y">fatcat:odpnldls7jhetgh23zvss5lm6y</a> </span>
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Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review [article]

Irene Li, Jessica Pan, Jeremy Goldwasser, Neha Verma, Wai Pan Wong, Muhammed Yavuz Nuzumlalı, Benjamin Rosand, Yixin Li, Matthew Zhang, David Chang, R. Andrew Taylor, Harlan M. Krumholz (+1 others)
<span title="2021-07-07">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this survey paper, we summarize current neural NLP methods for EHR applications.  ...  We focus on a broad scope of tasks, namely, classification and prediction, word embeddings, extraction, generation, and other topics such as question answering, phenotyping, knowledge graphs, medical dialogue  ...  Named Entity Recognition Named Entity Recognition (NER) is the task of determining whether tokens or spans in a text correspond to certain "named entities" of interest, such as medications and diseases  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.02975v1">arXiv:2107.02975v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nayhw7gadfdzrovycdkvzy75pi">fatcat:nayhw7gadfdzrovycdkvzy75pi</a> </span>
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A clinical trials corpus annotated with UMLS entities to enhance the access to evidence-based medicine

Leonardo Campillos-Llanos, Ana Valverde-Mateos, Adrián Capllonch-Carrión, Antonio Moreno-Sandoval
<span title="2021-02-22">2021</span> <i title="Springer (Biomed Central Ltd.)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bnylrk2y7bfnrn7u2f2vjkx7ta" style="color: black;">BMC Medical Informatics and Decision Making</a> </i> &nbsp;
As use case, we run medical entity recognition experiments with neural network models.  ...  To contribute with a new dataset for this domain, we collected the Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) corpus.  ...  Isabel Segura-Bedmar for their advice and domain expertise, which inspired us to annotate texts from clinical trials and helped us with some technical details for computing the inter-annotator agreement  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12911-021-01395-z">doi:10.1186/s12911-021-01395-z</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33618727">pmid:33618727</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qimk237qerb35bngvlsxjnrlq4">fatcat:qimk237qerb35bngvlsxjnrlq4</a> </span>
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NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction [article]

Lukas Lange, Xiang Dai, Heike Adel, Jannik Strötgen
<span title="2020-10-23">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
approaches with context-aware embeddings.  ...  In this paper, we describe our system for the CANTEMIST shared task, which is able to extract, normalize and rank ICD codes from Spanish electronic health records using neural sequence labeling and parsing  ...  S3 : A bia ne model with multilingual BERT and fastText embeddings for nested named entity recognition. S4 : A similar bia ne model trained on the development set in addition to the training set.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.12322v1">arXiv:2010.12322v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yp4a2sdz2fa4fb5of3r5oanrfa">fatcat:yp4a2sdz2fa4fb5of3r5oanrfa</a> </span>
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Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach

Oswaldo Solarte Pabón, Orlando Montenegro, Maria Torrente, Alejandro Rodríguez González, Mariano Provencio, Ernestina Menasalvas
<span title="2022-03-07">2022</span> <i title="PeerJ"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zs2czkfyybggbpvr26rbyxpsjy" style="color: black;">PeerJ Computer Science</a> </i> &nbsp;
In this paper, we propose a deep learning-based approach for both negation and uncertainty detection in clinical texts written in Spanish.  ...  The proposed approach shows the feasibility of deep learning-based methods to detect negation and uncertainty in Spanish clinical texts.  ...  In our approach, we only use information from word embeddings and contextual embeddings to train deep learning-based models.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7717/peerj-cs.913">doi:10.7717/peerj-cs.913</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35494817">pmid:35494817</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC9044225/">pmcid:PMC9044225</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wuk6oblpdva6zfzt6a4ie6stzm">fatcat:wuk6oblpdva6zfzt6a4ie6stzm</a> </span>
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A Survey on Recent Advances in Sequence Labeling from Deep Learning Models [article]

Zhiyong He, Zanbo Wang, Wei Wei, Shanshan Feng, Xianling Mao, Sheng Jiang
<span title="2020-11-13">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
., part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc.  ...  In this paper, we aim to present a comprehensive review of existing deep learning-based sequence labeling models, which consists of three related tasks, e.g., part-of-speech tagging, named entity recognition  ...  [113] employ stacked Gated Convolutional Neural Networks(GCNN) for named entity recognition, which extend the convolutional layer with gating mechanism.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.06727v1">arXiv:2011.06727v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lbephd7kdjh6libg2v5xju7lri">fatcat:lbephd7kdjh6libg2v5xju7lri</a> </span>
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Current Approaches and Applications in Natural Language Processing

Arturo Montejo-Ráez, Salud María Jiménez-Zafra
<span title="2022-05-11">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
Artificial Intelligence has gained a lot of popularity in recent years thanks to the advent of, mainly, Deep Learning techniques [...]  ...  The paper summarizes the current status of named entity recognition techniques and clinical relationship extraction in the clinical domain, discussing the existing models for the two tasks and their performances  ...  A proposal for personality recognition relying on the dominance, influence, steadiness, and compliance (DISC) model together with a Bag-of-Words model of language is presented in [6] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app12104859">doi:10.3390/app12104859</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yhoyyoqcazflrbx7veksnkrrdq">fatcat:yhoyyoqcazflrbx7veksnkrrdq</a> </span>
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Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts [article]

Isabel Segura-Bedmar, David Camino-Perdonas, Sara Guerrero-Aspizua
<span title="2021-11-11">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In particular, this model obtains an F1-score of 85.2% for rare diseases, outperforming all the other models.  ...  The paper explores the use of several deep learning techniques such as Bidirectional Long Short Term Memory (BiLSTM) networks or deep contextualized word representations based on Bidirectional Encoder  ...  [13] used a deep convolutional neural network (CNN). In addition to word embeddings, the authors also exploited character embeddings and lexicon feature embeddings to represent the texts.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.00343v2">arXiv:2109.00343v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e6wsbi5kenar7kukq7z6mewuke">fatcat:e6wsbi5kenar7kukq7z6mewuke</a> </span>
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On the Use of Parsing for Named Entity Recognition

Miguel A. Alonso, Carlos Gómez-Rodríguez, Jesús Vilares
<span title="2021-01-25">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
Named entity recognition (NER) is the task of identifying the entities that appear in a text.  ...  However, parsing has been a relatively little-used technique in NER systems, since most of them have chosen to consider shallow approaches to deal with text.  ...  Named Entity Recognition Named entity recognition is the task of locating references to entities in texts and classifying them into predefined categories.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app11031090">doi:10.3390/app11031090</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6suu3nnvonfzbmmi3uywcts5xq">fatcat:6suu3nnvonfzbmmi3uywcts5xq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210129161755/https://res.mdpi.com/d_attachment/applsci/applsci-11-01090/article_deploy/applsci-11-01090-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0f/ae/0faeccc859be55ba05413d1994574c9567ac7fa8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app11031090"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>
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