ArticleJournal of healthcare engineering2020
Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining.
Article in Journal of healthcare engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Information extraction from green channel textual records on expressways using hybrid deep learning.Scientific reports · 2024Article
- Chest computed tomography for patients with sepsis in the emergency intensive care unit.Scientific data · 2024Article
- MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records.Interdisciplinary sciences, computational life sciences · 2024Article
- Establishment of a Chinese critical care database from electronic healthcare records in a tertiary care medical center.Scientific data · 2023Article
- A BERT-Span model for Chinese named entity recognition in rehabilitation medicine.PeerJ. Computer science · 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
- A multi-layer soft lattice based model for Chinese clinical named entity recognition.BMC medical informatics and decision making · 2022Article
- Deep learning-based methods for natural hazard named entity recognition.Scientific reports · 2022Article
- A Core Drug Discovery Framework from Large-Scale Literature for Cold Pathogenic Disease Treatment in Traditional Chinese Medicine.Journal of healthcare engineering · 2021Article
- TCMNER and PubMed: A Novel Chinese Character-Level-Based Model and a Dataset for TCM Named Entity Recognition.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Clinical named entity recognition is the basic task of mining electronic medical records text, which are with some challenges containing the language features of Chinese electronic medical records text with many compound entities, serious missing sentence components, and unclear entity boundary. Moreover, the corpus of Chinese electronic medical records is difficult to obtain. Methods: Aiming at these characteristics of Chinese electronic medical records, this study proposed a Chinese clinical entity recognition model based on deep learning pretraining. The model used word embedding from domain corpus and fine-tuning of entity recognition model pretrained by relevant corpus. Then BiLSTM and Transformer are, respectively, used as feature extractors to identify four types of clinical entities including diseases, symptoms, drugs, and operations from the text of Chinese electronic medical records. Results: 75.06% Macro- Conclusions: These experiments show that the proposed Chinese clinical entity recognition model based on deep learning pretraining can effectively improve the recognition performance.
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