ArticleBMC medical informatics and decision making2019
A hybrid approach for named entity recognition in Chinese electronic medical record.
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 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 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
- A Chinese Named Entity Recognition Dataset for Intangible Cultural Heritage.Scientific data · 2026Article
- Chinese medical named entity recognition integrating adversarial training and feature enhancement.Scientific reports · 2025Article
- Recognition of Chinese Electronic Medical Records for Rehabilitation Robots: Information Fusion Classification Strategy.Sensors (Basel, Switzerland) · 2024Article
- MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records.Interdisciplinary sciences, computational life sciences · 2024Article
- Improved XLNet modeling for Chinese named entity recognition of edible fungus.Frontiers in plant science · 2024Article
- Enhancing efficiency and capacity of telehealth services with intelligent triage: a bidirectional LSTM neural network model employing character embedding.BMC medical informatics and decision making · 2023Article
- Named Entity Recognition in Electronic Health Records: A Methodological Review.Healthcare informatics research · 2023Article
- Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records.Frontiers in neuroscience · 2023Article
- Prediction and analysis of time series data based on granular computing.Frontiers in computational neuroscience · 2023Article
- A BERT-Span model for Chinese named entity recognition in rehabilitation medicine.PeerJ. Computer science · 2023Article
- Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition.Frontiers in neuroscience · 2023Article
- Text Classification of Cancer Clinical Trial Eligibility Criteria.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- An imConvNet-based deep learning model for Chinese medical named entity recognition.BMC medical informatics and decision making · 2022Article
- Identifying stroke diagnosis-related features from medical imaging reports to improve clinical decision-making support.BMC medical informatics and decision making · 2022Article
- The Construction Model of the TCM Clinical Knowledge Coding Database Based on Knowledge Organization.BioMed research international · 2022Article
- Multi-task learning for Chinese clinical named entity recognition with external knowledge.BMC medical informatics and decision making · 2021Article
- Extraction of Traditional Chinese Medicine Entity: Design of a Novel Span-Level Named Entity Recognition Method With Distant Supervision.JMIR medical informatics · 2021Article
- Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study.JMIR medical informatics · 2020Article
- KGHC: a knowledge graph for hepatocellular carcinoma.BMC medical informatics and decision making · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the rapid spread of electronic medical records and the arrival of medical big data era, the application of natural language processing technology in biomedicine has become a hot research topic.
methodsIn this paper, firstly, BiLSTM-CRF model is applied to medical named entity recognition on Chinese electronic medical record. According to the characteristics of Chinese electronic medical records, obtain the low-dimensional word vector of each word in units of sentences. And then input the word vector to BiLSTM to realize automatic extraction of sentence features. And then CRF performs sentence-level word tagging. Secondly, attention mechanism is added between the BiLSTM and the CRF to construct Attention-BiLSTM-CRF model, which can leverage document-level information to alleviate tagging inconsistency. In addition, this paper proposes an entity auto-correct algorithm to rectify entities according to historical entity information. At last, a drug dictionary and post-processing rules are well-built to rectify entities, to further improve performance.
resultsThe final F1 scores of the BiLSTM-CRF and Attention-BiLSTM-CRF model on given test dataset are 90.15 and 90.82% respectively, both of which are higher than 89.26%, which is the best F1 score on the test dataset except ours.
conclusionOur approach can be used to recognize medical named entity on Chinese electronic medical records and achieves the state-of-the-art performance on the given test dataset.
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