基于BioBERT与BiLSTM的临床试验纳排标准命名实体识别
Named entity recognition of eligibility criteria for clinical trials based on BioBERT and BiLSTM
李盛青 1苏前敏 1黄继汉2
作者信息
- 1. 上海工程技术大学电子电气工程学院,上海 201620
- 2. 上海中医药大学药物临床研究中心,上海 201203
- 折叠
摘要
目的:提出一种基于BioBERT预训练模型的纳排标准命名实体识别方法(BioBERT-Att-BiLSTM-CRF),可自动提取临床试验相关信息,为高效制定纳排标准提供帮助.方法:结合UMLS医学语义网络和专家定义方式,制定医学实体标注规则,并建立命名实体识别语料库以明确实体识别任务.BioBERT-Att-BiLSTM-CRF首先将文本转换为BioBERT向量并输入至双向长短期记忆网络以捕捉上下文语义特征;同时运用注意力机制来提取关键特征;最终采用条件随机场解码并输出最优标签序列.结果:BioBERT-Att-BiLSTM-CRF在纳排标准命名实体识别数据集上的效果优于其他基准模型.结论:使用BioBERT-Att-BiLSTM-CRF能更高效地提取临床试验的纳排标准相关信息,从而增强临床试验注册数据的科学性,并为临床试验纳排标准的制定提供帮助.
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.
关键词
纳排标准/命名实体识别/双向长短期记忆网络/条件随机场/临床试验Key words
eligibility criteria/named entity recognition/bidirectional long short-term memory network/conditional random field/clinical trial引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据