Clinical Trial Information Extraction with BERT

TL;DR

This paper introduces CT-BERT, a BERT-based framework for extracting key entities from clinical trial texts, outperforming existing methods in accuracy and efficiency.

Contribution

The paper presents a novel BERT-based model specifically designed for clinical trial information extraction, improving upon previous baseline methods.

Findings

CT-BERT outperforms baseline models in entity recognition accuracy.

Fine-tuning BERT models enhances clinical trial text analysis.

The framework demonstrates superior performance in extracting eligibility criteria.

Abstract

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.