ArticleJMIR medical informatics2019
Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study.
Article in JMIR medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- TCMSF: A Construction Framework of Traditional Chinese Medicine Syndrome Ancient Book Knowledge Graph.Methods of information in medicine · 2024Article
- Health Communication through Positive and Solidarity Messages Amid the COVID-19 Pandemic: Automated Content Analysis of Facebook Uses.International journal of environmental research and public health · 2022Article
- Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records.BMC medical informatics and decision making · 2022Article
- TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2022Article
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation Using Combined Contextual Embeddings.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2022Article
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2022Article
- Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Hybrid Deep Learning for Medication-Related Information Extraction From Clinical Texts in French: MedExt Algorithm Development Study.JMIR medical informatics · 2021Article
- Comparing general and specialized word embeddings for biomedical named entity recognition.PeerJ. Computer science · 2021Article
- Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set.Applied soft computing · 2020Article
- Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation.JMIR medical informatics · 2020Article
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3 authors.
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Abstract
backgroundNamed entity recognition (NER) is a key step in clinical natural language processing (NLP). Traditionally, rule-based systems leverage prior knowledge to define rules to identify named entities. Recently, deep learning-based NER systems have become more and more popular. Contextualized word embedding, as a new type of representation of the word, has been proposed to dynamically capture word sense using context information and has proven successful in many deep learning-based systems in either general domain or medical domain. However, there are very few studies that investigate the effects of combining multiple contextualized embeddings and prior knowledge on the clinical NER task.
objectiveThis study aims to improve the performance of NER in clinical text by combining multiple contextual embeddings and prior knowledge.
methodsIn this study, we investigate the effects of combining multiple contextualized word embeddings with classic word embedding in deep neural networks to predict named entities in clinical text. We also investigate whether using a semantic lexicon could further improve the performance of the clinical NER system.
resultsBy combining contextualized embeddings such as ELMo and Flair, our system achieves the F-1 score of 87.30% when only training based on a portion of the 2010 Informatics for Integrating Biology and the Bedside NER task dataset. After incorporating the medical lexicon into the word embedding, the F-1 score was further increased to 87.44%. Another finding was that our system still could achieve an F-1 score of 85.36% when the size of the training data was reduced to 40%.
conclusionsCombined contextualized embedding could be beneficial for the clinical NER task. Moreover, the semantic lexicon could be used to further improve the performance of the clinical NER system.
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