Joint Extraction of Clinical Entities and Relations Using Multi-head Selection Method
Joint Extraction of Clinical Entities and Relations Using Multi-head Selection Method
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DOI：
10.1109/ialp54817.2021.9675275
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Xintao Fang;Yuting Song;Akira Maeda
中科院分区：
文献类型：
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作者：
Xintao Fang;Yuting Song;Akira Maeda
从非结构化病历中提取实体和关系的研究越来越受到人们的关注。除了现有的传统方法外，还提出了用于实体和关系提取的深度学习方法。然而，以前的工作对临床实体和关系提取没有考虑临床实体之间的多种关系，这往往存在于临床文本。为了处理多个关系，我们提出了使用多头选择方法进行临床实体和关系提取。由于预训练的语言模型已被证明对临床实体和关系提取有效，因此我们将预训练的语言模型与多头模型集成，以联合提取临床实体和关系。实验结果表明，该模型在i2 b2/VA 2010和n2 c2 2018挑战数据集上都能有效地进行实体和关系提取，并且在n2 c2 2018挑战中优于排名靠前的系统。我们还评估了四种现有预训练语言模型对临床实体和关系提取性能的影响。特定领域的预训练语言模型提高了临床实体和关系提取的性能。在BERT和使用BERT-CNN模块而不是BERT的单词系统来表示整个单词的BERT-BERT之间，我们发现BERT在临床实体和关系的联合提取方面优于BERT-BERT。
The extraction of entities and relations from unstructured clinical records has been attracting increasing attention. In addition to the existing traditional methods, deep learning methods have also been proposed for entity and relation extraction. However, previous work on clinical entity and relation extraction did not consider the multiple relations between clinical entities, which often exist in clinical texts. To deal with multiple relations, we propose using a multi-head selection method for clinical entity and relation extraction. As pre-trained language models have been shown to be effective for clinical entity and relation extraction, we integrate a pre-trained language model with a multi-head model to jointly extract clinical entities and relations. The experimental results show that the proposed model is effective for entity and relation extraction on both the i2b2/VA 2010 and n2c2 2018 challenge datasets and outperforms the top-ranking systems in the n2c2 2018 challenge. We also evaluate the impact of four existing pre-trained language models on clinical entity and relation extraction performance. The domain-specific pre-trained language model improves the performance of clinical entity and relation extraction. Between BERT and CharacterBERT, which uses a Character-CNN module instead of BERT's wordpiece system to represent entire words, we find that BERT outperforms CharacterBERT on joint extraction of clinical entities and relations.