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.
Cite
@article{arxiv.2110.10027,
title = {Clinical Trial Information Extraction with BERT},
author = {Xiong Liu and Greg L. Hersch and Iya Khalil and Murthy Devarakonda},
journal= {arXiv preprint arXiv:2110.10027},
year = {2021}
}
Comments
HealthNLP 2021, IEEE International Conference on Healthcare Informatics (ICHI 2021)