# Text Classification of Cancer Clinical Trial Eligibility Criteria

arXiv: https://arxiv.org/abs/2309.07812 · HTML: https://mlsanity.com/paper/2309.07812/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [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)
- [Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks](https://mlsanity.com/paper/1807.07425/) (2018)
- [Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Service](https://mlsanity.com/paper/1910.07419/) (2019)
- [The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria](https://mlsanity.com/paper/2207.13757/) (2022)

## Built on by (most cited first, 7 of 7)

- [Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness](https://mlsanity.com/paper/2408.04585/) (2024)
- [Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks](https://mlsanity.com/paper/2405.11704/) (2024)
- [Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model](https://mlsanity.com/paper/2410.17545/) (2024)
- [Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases](https://mlsanity.com/paper/2403.17135/) (2024)
- [Using Large Language Models to Generate Clinical Trial Tables and Figures](https://mlsanity.com/paper/2409.12046/) (2024)
- [Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review](https://mlsanity.com/paper/2505.22280/) (2025)
- [A systematic review of trial-matching pipelines using large language models](https://mlsanity.com/paper/2509.19327/) (2025)

## Abstract (arXiv)

Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility is stated in natural language. A potential solution to this problem is to employ text classification methods for common types of eligibility criteria. In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained clinical trial BERT model. Our results demonstrate the feasibility of automatically classifying common exclusion criteria. Additionally, we demonstrate the value of a pre-trained language model specifically for clinical trials, which yields the highest average performance across all criteria.