Clinical Trial Information Extraction with BERT
This work addresses information extraction for clinical trial design, but it is incremental as it applies an existing method (BERT fine-tuning) to a specific domain.
The authors tackled the problem of extracting eligibility criteria from clinical trial documents by fine-tuning pre-trained BERT models, resulting in CT-BERT outperforming baseline methods like attention-based BiLSTM and Criteria2Query.
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.