# BERT-based Ranking for Biomedical Entity Normalization

arXiv: https://arxiv.org/abs/1908.03548 · HTML: https://mlsanity.com/paper/1908.03548/

## Key facts

- Cited by 29 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 9 Aug 2019.

## Built on by (most cited first, 29 of 29)

- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Knowledge-Rich Self-Supervision for Biomedical Entity Linking](https://mlsanity.com/paper/2112.07887/) (2021)
- [Transformers in Healthcare: A Survey](https://mlsanity.com/paper/2307.00067/) (2023)
- [Fast and Effective Biomedical Entity Linking Using a Dual Encoder](https://mlsanity.com/paper/2103.05028/) (2021)
- [BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks](https://mlsanity.com/paper/2109.02237/) (2021)
- [Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking](https://mlsanity.com/paper/2307.01137/) (2023)
- [HypKG: Hypergraph-based Knowledge Graph Contextualization for Precision Healthcare](https://mlsanity.com/paper/2507.19726/) (2025)
- [Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking](https://mlsanity.com/paper/2302.07189/) (2023)
- [Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution](https://mlsanity.com/paper/2305.11315/) (2023)
- [Comparison of biomedical relationship extraction methods and models for knowledge graph creation](https://mlsanity.com/paper/2201.01647/) (2022)
- [High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models](https://mlsanity.com/paper/2403.05920/) (2024)
- [Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model](https://mlsanity.com/paper/2108.09211/) (2021)
- [Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representations](https://mlsanity.com/paper/2112.05596/) (2021)
- [A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes](https://mlsanity.com/paper/2406.14757/) (2024)
- [High-Throughput Phenotyping of Clinical Text Using Large Language Models](https://mlsanity.com/paper/2408.01214/) (2024)
- [GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Prediction](https://mlsanity.com/paper/2112.03002/) (2021)
- [Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality](https://mlsanity.com/paper/2201.10463/) (2022)
- [Fine-Tuning BERT for Automatic ADME Semantic Labeling in FDA Drug Labeling to Enhance Product-Specific Guidance Assessment](https://mlsanity.com/paper/2207.12376/) (2022)
- [Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives](https://mlsanity.com/paper/2506.12242/) (2025)
- [CACER: Clinical Concept Annotations for Cancer Events and Relations](https://mlsanity.com/paper/2409.03905/) (2024)
- [Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linking](https://mlsanity.com/paper/2505.19722/) (2025)
- [Automating the Compilation of Potential Core-Outcomes for Clinical Trials](https://mlsanity.com/paper/2101.04076/) (2021)
- [A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization](https://mlsanity.com/paper/2101.09101/) (2021)
- [Named Entity Normalization Model Using Edge Weight Updating Neural Network: Assimilation Between Knowledge-Driven Graph and Data-Driven Graph](https://mlsanity.com/paper/2106.07549/) (2021)
- [Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique](https://mlsanity.com/paper/2209.12177/) (2022)
- [Knowledge-injected Prompt Learning for Chinese Biomedical Entity Normalization](https://mlsanity.com/paper/2308.12025/) (2023)
- [Search-Optimized Quantization in Biomedical Ontology Alignment](https://mlsanity.com/paper/2507.13742/) (2025)
- [Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints](https://mlsanity.com/paper/2509.01899/) (2025)
- [MetaBench: A Multi-task Benchmark for Assessing LLMs in Metabolomics](https://mlsanity.com/paper/2510.14944/) (2025)

## Abstract (arXiv)

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.