Text Classification of Cancer Clinical Trial Eligibility Criteria

Document detail

ID

oai:arXiv.org:2309.07812

Topic

Computer Science - Computation and... Computer Science - Machine Learnin...

Author

Yang, Yumeng Jayaraj, Soumya Ludmir, Ethan B Roberts, Kirk

Year

2023

listing date

9/20/2023

Keywords

cancer language eligibility clinical criteria

Metrics

Abstract

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.

;Comment: AMIA Annual Symposium Proceedings 2023

Yang, Yumeng,Jayaraj, Soumya,Ludmir, Ethan B,Roberts, Kirk, 2023, Text Classification of Cancer Clinical Trial Eligibility Criteria