Abstract

Info

Title:: Named entity recognition of eligibility criteria for clinical trials based on BioBERT and BiLSTM

Author(s):: LI Shengqing1; SU Qianmin1; HUANG Jihan2; 1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China

Keywords:: Keywords: eligibility criteria named entity recognition bidirectional long short-term memory network conditional random field clinical trial

Abstract:: Abstract: Objective To present a named entity recognition method referred to as BioBERT-Att-BiLSTM-CRF for eligibility criteria based on the BioBERT pretrained model. The method can automatically extract relevant information from clinical trials and provide assistance in efficiently formulating eligibility criteria. Methods Based on the UMLS medical semantic network and expert-defined rules, the study established medical entity annotation rules and constructed a named entity recognition corpus to clarify the entity recognition task. BioBERT-Att-BiLSTM-CRF converted the text into BioBERT vectors and inputted them into a bidirectional long short-term memory network to capture contextual semantic features. Meanwhile, attention mechanisms were applied to extract key features, and a conditional random field was used for decoding and outputting the optimal label sequence. Results BioBERT-Att-BiLSTM-CRF outperformed other baseline models on the eligibility criteria named entity recognition dataset. Conclusion BioBERT-Att-BiLSTM-CRF can efficiently extract eligibility criteria-related information from clinical trials, thereby enhancing the scientific validity of clinical trial registration data and providing assistance in the formulation of eligibility criteria for clinical trials.