# Clinical Trial Information Extraction with BERT

arXiv: https://arxiv.org/abs/2110.10027 · HTML: https://mlsanity.com/paper/2110.10027/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 11 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Applications of artificial intelligence in drug development using real-world data](https://mlsanity.com/paper/2101.08904/) (2021)
- [Information Extraction of Clinical Trial Eligibility Criteria](https://mlsanity.com/paper/2006.07296/) (2020)
- [Attention-Based LSTM Network for COVID-19 Clinical Trial Parsing](https://mlsanity.com/paper/2012.10063/) (2020)

## Built on by (most cited first, 8 of 8)

- [Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases](https://mlsanity.com/paper/2403.17135/) (2024)
- [A Survey on LLM-Assisted Clinical Trial Recruitment](https://mlsanity.com/paper/2506.15301/) (2025)
- [Entity Recognition from Colloquial Text](https://mlsanity.com/paper/2401.04853/) (2024)
- [DAEDRA: A language model for predicting outcomes in passive pharmacovigilance reporting](https://mlsanity.com/paper/2402.10951/) (2024)
- [CTBench: A Comprehensive Benchmark for Evaluating Language Model Capabilities in Clinical Trial Design](https://mlsanity.com/paper/2406.17888/) (2024)
- [AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows](https://mlsanity.com/paper/2602.00052/) (2026)
- [Customizing Knowledge Graph Embedding to Improve Clinical Study Recommendation](https://mlsanity.com/paper/2212.14102/) (2022)
- [The Last Mile of Coding Agents for Long-Document Tasks: An Empirical Study on Clinical Trial Protocol Digitization](https://mlsanity.com/paper/2610.18507/) (2026)

## Abstract (arXiv)

Natural language processing (NLP) of clinical trial documents can be useful in new trial design. Here we identify entity types relevant to clinical trial design and propose a framework called CT-BERT for information extraction from clinical trial text. We trained named entity recognition (NER) models to extract eligibility criteria entities by fine-tuning a set of pre-trained BERT models. We then compared the performance of CT-BERT with recent baseline methods including attention-based BiLSTM and Criteria2Query. The results demonstrate the superiority of CT-BERT in clinical trial NLP.