Named Entity Recognition for Clinical Portuguese Corpus with Conditional Random Fields and Semantic Groups

Souza, João Vitor Andrioli de; Gumiel, Yohan Bonescki; Oliveira, Lucas Emanuel Silva e; Moro, Cláudia Maria Cabral

doi:10.5753/sbcas.2019.6269

Cited by 6 publications

(7 citation statements)

References 7 publications

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“…By providing a contextualized word representation and taking advantage of the transformer architecture, BERT-based language models have become a new paradigm for NLP tasks . The use of BERT-base models in our work had a positive impact on the results when compared to previous works with traditional machine learning algorithms and word embeddings for NER in Portuguese clinical text (de Souza et al, 2019;Lopes et al, 2019). For examples, de Souza et al (2019) evaluated three groups of entities from the Sem-ClinBr corpus using CRF, without any word embedding.…”

Section: Effect Of the Contextualized Language Modelmentioning

confidence: 91%

“…The use of BERT-base models in our work had a positive impact on the results when compared to previous works with traditional machine learning algorithms and word embeddings for NER in Portuguese clinical text (de Souza et al, 2019;Lopes et al, 2019). For examples, de Souza et al (2019) evaluated three groups of entities from the Sem-ClinBr corpus using CRF, without any word embedding. AS shown in Table 3, they obtained for Disorder 0.65 of F1-score, compared to our 0.79; for Procedure, they achieved 0.60 compared to our 0.70 and for Drug, they achieved 0.42 compared to our 0.91.…”

Section: Effect Of the Contextualized Language Modelmentioning

confidence: 91%

“…In Portuguese, Lopes et al (2019) proposed a fastText model trained with clinical texts, which achieved higher results when compared to out-ofdomain embeddings. In a recent work, de Souza et al (2019) explored the CRF algorithm for the NER task on SemClinBr (Oliveira et al, 2020), the same annotated corpus we used in this work. They classified three clinical entities (Disorders, Procedures and Chemicals and Drugs) and some medical text abbreviations, achieving promising results.…”

Section: Introductionmentioning

confidence: 99%

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BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition

Schneider¹,

Souza²,

Knafou³

et al. 2020

Proceedings of the 3rd Clinical Natural Language Processing Workshop

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With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72%, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.

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Section: Effect Of the Contextualized Language Modelmentioning

confidence: 91%

Section: Effect Of the Contextualized Language Modelmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition

Schneider¹,

Souza²,

Knafou³

et al. 2020

Proceedings of the 3rd Clinical Natural Language Processing Workshop

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“…This type of algorithm (i.e., NER) can support many methods, such as medical concept extraction, biomedical summarization algorithms, and clinical decision support systems. Souza et al [ 57 ] described their preliminary work with promising results on exploring conditional random field (CRF) algorithms to perform NER in clinical pt-br texts. They used different fragments of our corpus and different annotation granularities (STYs and SGRs) to train and evaluate their model.…”

Section: Resultsmentioning

confidence: 99%

SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks

et al. 2022

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Background The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. Methods In this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations. Results This study resulted in SemClinBr, a corpus that has 1000 clinical notes, labeled with 65,117 entities and 11,263 relations. In addition, both negation cues and medical abbreviation dictionaries were generated from the annotations. The average annotator agreement score varied from 0.71 (applying strict match) to 0.92 (considering a relaxed match) while accepting partial overlaps and hierarchically related semantic types. The extrinsic evaluation, when applying the corpus to two downstream NLP tasks, demonstrated the reliability and usefulness of annotations, with the systems achieving results that were consistent with the agreement scores. Conclusion The SemClinBr corpus and other resources produced in this work can support clinical NLP studies, providing a common development and evaluation resource for the research community, boosting the utilization of EHRs in both clinical practice and biomedical research. To the best of our knowledge, SemClinBr is the first available Portuguese clinical corpus.

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“…In fact, few studies are focusing on clinical text processing in general, e.g. (Oliveira et al, 2019;de Souza et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Defining a state-of-the-art POS-tagging environment for Brazilian Portuguese clinical texts

Oliveira

Gumiel

et al. 2020

Res. Biomed. Eng.

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Purpose Natural language processing techniques are essential for unlocking patients' data from electronic health records. An important NLP task is the ability to recognize morphosyntactic information from the texts, a process called part-of-speech (POS) tagging. Currently, neural network architectures are the state-of-the-art method, although there is a lack of studies exploiting this approach within Brazilian Portuguese clinical texts. The objective of this study is to define a state-of-the-art POS-tagging environment for Brazilian Portuguese clinical texts. Methods We reviewed multiple neural network-based POS-tagging algorithms, and the Flair tool was selected due to its exceptional performance in the journalistic domain, as there is any specific algorithm to Portuguese clinical texts. We executed a normalization process on available corpora from multiple domains (two journalistic, one biomedical, one clinical, and a new corpus composed of all three of these). The Flair algorithm was trained with all corpora, generating five models, which were evaluated with all domains. Results The clinical model achieved 92.39% accuracy (previous POS-tagging clinical work reached 91.5%); the biomedical model achieved 97.9% accuracy. All the models were assessed on their own test set. Conclusion We developed a new state-of-the-art modeling environment for POS tagging of Brazilian Portuguese clinical texts and achieved comparable results to other state-of-the-art studies in journalistic contexts.

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Named Entity Recognition for Clinical Portuguese Corpus with Conditional Random Fields and Semantic Groups

Cited by 6 publications

References 7 publications

BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition

BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition

SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks

Defining a state-of-the-art POS-tagging environment for Brazilian Portuguese clinical texts

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