Related papers: Clinical Trial Information Extraction with BERT

Clinical Trial Information Extraction with BERT

URL: http://arxiv.org/abs/2110.10027v1
Date: Sat, 11 Sep 2021 17:15:10 GMT
Title: Clinical Trial Information Extraction with BERT
Authors: Xiong Liu, Greg L. Hersch, Iya Khalil, Murthy Devarakonda
Abstract summary: We 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. The results demonstrate the superiority of CT-BERT in clinical trial NLP.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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