From Electronic Health Records to Terminology Base:A Novel Knowledge Base Enrichment Approach
From Electronic Health Records to Terminology Base:A Novel Knowledge Base Enrichment Approach
复制标题
从电子健康记录到术语库：一种新颖的知识库丰富方法
DOI：
10.1016/j.jbi.2020.103628
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
周扬名
中科院分区：
文献类型：
--
作者：
张佳影;张知行;张欢欢;马致远;叶琪;何萍;周扬名
丰富术语库是一个重要而持续的过程，因为正式术语可以被重新命名，新的术语别名一直在出现。电子健康记录(EHR)是临床研究和实践的基本来源，是结核病防治的潜在补充。将EHR中的一组外部术语与TB对齐的任务可以被视为没有结构信息的实体对齐。传统的方法主要是利用多个知识库的内部结构信息来映射知识库中的实体及其对应关系。然而，EHR中的外部术语是独立的临床术语，缺乏相互联系。为了在这种情况下实现实体对齐，我们提出了一种新的基于语义和结构嵌入的关联预测方法(S2ERP)。为了获得外部术语的语义嵌入，我们将它们与形式实体一起馈送到预先训练的语言模型中。同时，使用图卷积网络来获取TB中同义词和下义词的结构嵌入。然后，S2ERP结合这两个嵌入来衡量相关性。对上海医院发展中心38家三甲医院的临床指标结核病的实验结果表明，该方法比基准方法在Hits@1中的性能提高了14.16%。
Enriching terminology base (TB) is an important and continuous process, since formal term can be renamed and new term alias emerges all the time. As a potential supplementary for TB enrichment, electronic health record (EHR) is a fundamental source for clinical research and practise. The task to align the set of external terms in EHRs to TB can be regarded as entity alignment without structure information. Conventional approaches mainly use internal structural information of multiple knowledge bases (KBs) to map entities and their counterparts among KBs. However, the external terms in EHRs are independent clinical terms, which lack of interrelations. To achieve entity alignment in this case, we proposed a novel automatic TB enrichment approach, named semantic & structure embeddings-based relevancy prediction (S2ERP). To obtain the semantic embedding of external terms, we fed them with formal entity into a pre-trained language model. Meanwhile, a graph convolutional network was used to obtain the structure embeddings of the synonyms and hyponyms in TB. Afterwards, S2ERP combines both embeddings to measure the relevancy. Experimental results on clinical indicator TB, collected from 38 top-class hospitals of Shanghai Hospital Development Center, showed that the proposed approach outperforms baseline methods by 14.16% inHits@1.