Cross-lingual semantic annotation of biomedical literature: experiments in Spanish and English

Pérez, Naiara; Accuosto, Pablo; Bravo, Àlex; Cuadros, Montse; Martínez-Garcia, Eva; Saggion, Horacio; Rigau, Germán

doi:10.1093/bioinformatics/btz853

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…KE was performed using the tool presented by Pérez et al [6], [7] to normalize biomedical terms of EHRs with the Unified Medical Language System (UMLS) Metathesaurus, a large, multi-lingual compendium of biomedical and health-related terminologies. The 'medical history' field for each patient was selected as the input text to be processed with this tool.…”

Section: Methodsmentioning

confidence: 99%

Hospital Readmission Prediction via Keyword Extraction and Sentiment Analysis on Clinical Notes

Zubillaga

Laccourreye

Kerexeta

et al. 2022

Studies in Health Technology and Informatics

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Unplanned hospital readmission is a problem that affects hospitals worldwide and is due to different factors. The identification of those factors can help determine which patients are at greater risk of hospital readmission for early intervention. Our end goal is to predict and identify patterns to (i) feed a decision support system for efficient management of patients and resources and (ii) detect patients at high risk of 30-days readmission enabling preventive actions to improve management of hospital discharges. This study aims to analyze whether natural language processing and specifically keyword extractions tools and sentiment analysis can support 30-days readmission prediction. Features extracted from medical history notes and discharge reports were used to train a Logistic Regression model. The resulting model obtains an AUC of 0.63 indicating that the sentiment polarity score of the discharge report and several of the extracted keywords are representative features to consider.

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Section: Methodsmentioning

confidence: 99%

Hospital Readmission Prediction via Keyword Extraction and Sentiment Analysis on Clinical Notes

Zubillaga

Laccourreye

Kerexeta

et al. 2022

Studies in Health Technology and Informatics

View full text Add to dashboard Cite

show abstract

“…The proposed model performs similarly to commercial translators (such as Google and Bing) on these four languages. Perez et al [177] compared the effectiveness of three approaches in automatic annotation of biomedical texts in Spanish: information retrieval and concept disambiguation, machine translation (annotating in English and translating into Spanish), and a hybrid of the two. The hybrid approach performed the best of the three, achieving an average F1 score of 0.632.…”

Section: Multilingualitymentioning

confidence: 99%

Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review

Li¹,

Pan²,

Goldwasser³

et al. 2021

Preprint

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Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and research. Despite this central role, EHRs are notoriously difficult to process automatically. Well over half of the information stored within EHRs is in the form of unstructured text (e.g. provider notes, operation reports) and remains largely untapped for secondary use. Recently, however, newer neural network and deep learning approaches to Natural Language Processing (NLP) have made considerable advances, outperforming traditional statistical and rule-based systems on a variety of tasks. 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, multilinguality, interpretability, etc.CCS Concepts: • General and reference → Surveys and overviews; • Computing methodologies → Natural language processing; Machine learning algorithms.

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“…Reference [14] presented UMLSMapper, a lexically/knowledge-driven system that relies on several terminological resources from UMLS. In [15], UMLSMapper is combined with crosslingual approaches obtaining very promising results. Proposals for Spanish NER based on Bidirectional Long Short-Term Memory (Bi-LSTM) networks and Conditional Random Fields (CRFs) are presented in [7,16].…”

Section: Introductionmentioning

confidence: 99%

Improving Medical Entity Recognition in Spanish by Means of Biomedical Language Models

Villaplana,

Martínez,

Montalvo

2023

Electronics

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Named Entity Recognition (NER) is an important task used to extract relevant information from biomedical texts. Recently, pre-trained language models have made great progress in this task, particularly in English language. However, the performance of pre-trained models in the Spanish biomedical domain has not been evaluated in an experimentation framework designed specifically for the task. We present an approach for named entity recognition in Spanish medical texts that makes use of pre-trained models from the Spanish biomedical domain. We also use data augmentation techniques to improve the identification of less frequent entities in the dataset. The domain-specific models have improved the recognition of name entities in the domain, beating all the systems that were evaluated in the eHealth-KD challenge 2021. Language models from the biomedical domain seem to be more effective in characterizing the specific terminology involved in this task of named entity recognition, where most entities correspond to the "concept" type involving a great number of medical concepts. Regarding data augmentation, only back translation has slightly improved the results. Clearly, the most frequent types of entities in the dataset are better identified. Although the domain-specific language models have outperformed most of the other models, the multilingual generalist model mBERT obtained competitive results.

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Cross-lingual semantic annotation of biomedical literature: experiments in Spanish and English

Cited by 11 publications

References 23 publications

Hospital Readmission Prediction via Keyword Extraction and Sentiment Analysis on Clinical Notes

Hospital Readmission Prediction via Keyword Extraction and Sentiment Analysis on Clinical Notes

Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review

Improving Medical Entity Recognition in Spanish by Means of Biomedical Language Models

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