ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
BERT-based Ranking for Biomedical Entity Normalization.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
54 citing papers in PubMed.
- Symptom Terminology Normalization in Traditional Chinese Medicine: Development and Evaluation of a 2-Stage Deep Learning Framework Based on Fine-Grained Semantic Classification.JMIR medical informatics · 2026Article
- Benchmarking GPT-5, LLaMA, and Mistral for Clinical Named Entity Recognition in Ophthalmology Progress Notes.Translational vision science & technology · 2026Article
- Classification of health product defect reports by deep learning.Scientific reports · 2026Article
- Transformer-based relation extraction and concept normalization using an annotated clinical trials corpus.Scientific data · 2026Article
- Comparative assessment of large language models in diabetic foot infection management: alignment with IWGDF/IDSA guidelines.Frontiers in endocrinology · 2026Article
- Generative Models and Sentence Transformers for the Recognition and Normalization of Continuous and Discontinuous Phenotype Mentions: Model Development and Evaluation.JMIR medical informatics · 2025Article
- AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia.Artificial intelligence in medicine · 2025Article
- Benchmarking Transformer Embedding Models for Biomedical Terminology Standardization.Machine learning with applications · 2025Article
- NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes.Medical & biological engineering & computing · 2025Article
- Search-optimized quantization in biomedical ontology alignment.Frontiers in artificial intelligence · 2025Article
- CACER: Clinical concept Annotations for Cancer Events and Relations.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Mapping Drug Terms via Integration of a Retrieval-Augmented Generation Algorithm with a Large Language Model.Healthcare informatics research · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Chemical entity normalization for successful translational development of Alzheimer's disease and dementia therapeutics.Journal of biomedical semantics · 2024Article
- NeighBERT: Medical Entity Linking Using Relation-Induced Dense Retrieval.Journal of healthcare informatics research · 2024Article
- Using Social Media as a Source of Real-World Data for Pharmaceutical Drug Development and Regulatory Decision Making.Drug safety · 2024Article
- Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research.Systematic reviews · 2024Article
- Contextual Word Embedding for Biomedical Knowledge Extraction: a Rapid Review and Case Study.Journal of healthcare informatics research · 2024Article
- Evaluating MedDRA-to-ICD terminology mappings.BMC medical informatics and decision making · 2024Article
- Ways to make artificial intelligence work for healthcare professionals: correspondence.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Developing high-performance entity normalization algorithms that can alleviate the term variation problem is of great interest to the biomedical community. Although deep learning-based methods have been successfully applied to biomedical entity normalization, they often depend on traditional context-independent word embeddings. Bidirectional Encoder Representations from Transformers (BERT), BERT for Biomedical Text Mining (BioBERT) and BERT for Clinical Text Mining (ClinicalBERT) were recently introduced to pre-train contextualized word representation models using bidirectional Transformers, advancing the state-of-the-art for many natural language processing tasks. In this study, we proposed an entity normalization architecture by fine-tuning the pre-trained BERT / BioBERT / ClinicalBERT models and conducted extensive experiments to evaluate the effectiveness of the pre-trained models for biomedical entity normalization using three different types of datasets. Our experimental results show that the best fine-tuned models consistently outperformed previous methods and advanced the state-of-the-art for biomedical entity normalization, with up to 1.17% increase in accuracy.
Identifiers
32477646PMC7233044What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.