ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
A Domain Knowledge-Enhanced LSTM-CRF Model for Disease Named Entity Recognition.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A multi-layer soft lattice based model for Chinese clinical named entity recognition.BMC medical informatics and decision making · 2022Article
- Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts.BMC bioinformatics · 2022Article
- Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records.BMC medical informatics and decision making · 2022Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
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Authors and funding
7 authors.
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Abstract
Disease named entity recognition (NER) is a critical task for most biomedical natural language processing (NLP) applications. For example, extracting diseases from clinical trial text can be helpful for patient profiling and other downstream applications such as matching clinical trials to eligible patients. Similarly, disease annotation in biomedical articles can help information search engines to accurately index them such that clinicians can easily find relevant articles to enhance their knowledge. In this paper, we propose a domain knowledge-enhanced long short-term memory network-conditional random field (LSTM-CRF) model for disease named entity recognition, which also augments a character-level convolutional neural network (CNN) and a character-level LSTM network for input embedding. Experimental results on a scientific article dataset show the effectiveness of our proposed models compared to state-of-the-art methods in disease recognition.
Identifiers
31259033PMC6568095What OpenQuestion holds
Registered trials
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