ArticleJMIR medical informatics2021
Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding-Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Review
- Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study.JMIR formative research · 2025Article
- Large Language Models for Adverse Drug Events: A Clinical Perspective.Journal of clinical medicine · 2025Review
- A review of recent artificial intelligence for traditional medicine.Journal of traditional and complementary medicine · 2025Review
- Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity.Scientific reports · 2025Observational
- Chinese Clinical Named Entity Recognition With Segmentation Synonym Sentence Synthesis Mechanism: Algorithm Development and Validation.JMIR medical informatics · 2024Article
- Post-marketing surveillance of anticancer drugs using natural language processing of electronic medical records.NPJ digital medicine · 2024Article
- Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study.JMIR medical informatics · 2024Article
- Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.JMIR medical informatics · 2024Article
- exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies.PeerJ. Computer science · 2024Article
- A Deep Learning Model for the Normalization of Institution Names by Multisource Literature Feature Fusion: Algorithm Development Study.JMIR formative research · 2023Article
- Automatic knowledge extraction from Chinese electronic medical records and rheumatoid arthritis knowledge graph construction.Quantitative imaging in medicine and surgery · 2023Article
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10 authors.
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Abstract
backgroundWith the increasing variety of drugs, the incidence of adverse drug events (ADEs) is increasing year by year. Massive numbers of ADEs are recorded in electronic medical records and adverse drug reaction (ADR) reports, which are important sources of potential ADR information. Meanwhile, it is essential to make latent ADR information automatically available for better postmarketing drug safety reevaluation and pharmacovigilance.
objectiveThis study describes how to identify ADR-related information from Chinese ADE reports.
methodsOur study established an efficient automated tool, named BBC-Radical. BBC-Radical is a model that consists of 3 components: Bidirectional Encoder Representations from Transformers (BERT), bidirectional long short-term memory (bi-LSTM), and conditional random field (CRF). The model identifies ADR-related information from Chinese ADR reports. Token features and radical features of Chinese characters were used to represent the common meaning of a group of words. BERT and Bi-LSTM-CRF were novel models that combined these features to conduct named entity recognition (NER) tasks in the free-text section of 24,890 ADR reports from the Jiangsu Province Adverse Drug Reaction Monitoring Center from 2010 to 2016. Moreover, the man-machine comparison experiment on the ADE records from Drum Tower Hospital was designed to compare the NER performance between the BBC-Radical model and a manual method.
resultsThe NER model achieved relatively high performance, with a precision of 96.4%, recall of 96.0%, and F1 score of 96.2%. This indicates that the performance of the BBC-Radical model (precision 87.2%, recall 85.7%, and F1 score 86.4%) is much better than that of the manual method (precision 86.1%, recall 73.8%, and F1 score 79.5%) in the recognition task of each kind of entity.
conclusionsThe proposed model was competitive in extracting ADR-related information from ADE reports, and the results suggest that the application of our method to extract ADR-related information is of great significance in improving the quality of ADR reports and postmarketing drug safety evaluation.
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