ArticleBMC medical informatics and decision making2019
Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 62 citations in OpenAlex.
- Medical named entity recognition based on domain knowledge and position encoding.BMC medical informatics and decision making · 2025Article
- Prediction Model for Risk of Death in Elderly Critically Ill Patients with Kidney Failure.Medicina (Kaunas, Lithuania) · 2025Article
- MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records.Interdisciplinary sciences, computational life sciences · 2024Article
- MMBERT: a unified framework for biomedical named entity recognition.Medical & biological engineering & computing · 2024Article
- Article
- Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition.Frontiers in neuroscience · 2023Article
- Named entity recognition of Chinese electronic medical records based on a hybrid neural network and medical MC-BERT.BMC medical informatics and decision making · 2022Article
- Construction of an Assisted Model Based on Natural Language Processing for Automatic Early Diagnosis of Autoimmune Encephalitis.Neurology and therapy · 2022Article
- Multi-task learning for Chinese clinical named entity recognition with external knowledge.BMC medical informatics and decision making · 2021Article
- Special issue of BMC medical informatics and decision making on health natural language processing.BMC medical informatics and decision making · 2019Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical entity recognition as a fundamental task of clinical text processing has been attracted a great deal of attention during the last decade. However, most studies focus on clinical text in English rather than other languages. Recently, a few researchers have began to study entity recognition in Chinese clinical text.
methodsIn this paper, a novel deep neural network, called attention-based CNN-LSTM-CRF, is proposed to recognize entities in Chinese clinical text. Attention-based CNN-LSTM-CRF is an extension of LSTM-CRF by introducing a CNN (convolutional neural network) layer after the input layer to capture local context information of words of interest and an attention layer before the CRF layer to select relevant words in the same sentence.
resultsIn order to evaluate the proposed method, we compare it with other two currently popular methods, CRF (conditional random field) and LSTM-CRF, on two benchmark datasets. One of the datasets is publically available and only contains contiguous clinical entities, and the other one is constructed by us and contains contiguous and discontiguous clinical entities. Experimental results show that attention-based CNN-LSTM-CRF outperforms CRF and LSTM-CRF.
conclusionsCNN and attention mechanism are individually beneficial to LSTM-CRF-based Chinese clinical entity recognition system, no matter whether contiguous clinical entities are considered. The conribution of attention mechanism is greater than CNN.
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