Transformer-Based Named Entity Recognition for Parsing Clinical Trial Eligibility Criteria.

ABSTRACT: The rapid adoption of electronic health records (EHRs) systems has made clinical data available in electronic format for research and for many downstream applications. Electronic screening of potentially eligible patients using these clinical databases for clinical trials is a critical need to improve trial recruitment efficiency. Nevertheless, manually translating free-text eligibility criteria into database queries is labor intensive and inefficient. To facilitate automated screening, free-text eligibility criteria must be structured and coded into a computable format using controlled vocabularies. Named entity recognition (NER) is thus an important first step. In this study, we evaluate 4 state-of-the-art transformer-based NER models on two publicly available annotated corpora of eligib

SUBMITTER: Tian S 

PROVIDER: S-EPMC8373041 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature