# Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases

arXiv: https://arxiv.org/abs/2403.17135 · HTML: https://mlsanity.com/paper/2403.17135/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 25 Mar 2024.

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

- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Medical Text Classification using Convolutional Neural Networks](https://mlsanity.com/paper/1704.06841/) (2017)
- [CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study](https://mlsanity.com/paper/2307.11346/) (2023)
- [The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria](https://mlsanity.com/paper/2207.13757/) (2022)
- [Clinical Trial Information Extraction with BERT](https://mlsanity.com/paper/2110.10027/) (2021)
- [Text Classification of Cancer Clinical Trial Eligibility Criteria](https://mlsanity.com/paper/2309.07812/) (2023)
- [Information Extraction of Clinical Trial Eligibility Criteria](https://mlsanity.com/paper/2006.07296/) (2020)

## Built on by (most cited first, 3 of 3)

- [Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generation](https://mlsanity.com/paper/2409.11703/) (2024)
- [Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks](https://mlsanity.com/paper/2405.11704/) (2024)
- [Using Large Language Models to Generate Clinical Trial Tables and Figures](https://mlsanity.com/paper/2409.12046/) (2024)

## Abstract (arXiv)

Clinical trials are pivotal in medical research, and NLP can enhance their success, with application in recruitment. This study aims to evaluate the generalizability of eligibility classification across a broad spectrum of clinical trials. Starting with phase 3 cancer trials, annotated with seven eligibility exclusions, then to determine how well models can generalize to non-cancer and non-phase 3 trials. To assess this, we have compiled eligibility criteria data for five types of trials: (1) additional phase 3 cancer trials, (2) phase 1 and 2 cancer trials, (3) heart disease trials, (4) type 2 diabetes trials, and (5) observational trials for any disease, comprising 2,490 annotated eligibility criteria across seven exclusion types. Our results show that models trained on the extensive cancer dataset can effectively handle criteria commonly found in non-cancer trials, such as autoimmune diseases. However, they struggle with criteria disproportionately prevalent in cancer trials, like prior malignancy. We also experiment with few-shot learning, demonstrating that a limited number of disease-specific examples can partially overcome this performance gap. We are releasing this new dataset of annotated eligibility statements to promote the development of cross-disease generalization in clinical trial classification.