The Australian E-Health Research Centre, CSIRO, Australia.
AMIA Annu Symp Proc. 2022 Feb 21;2021:910-919. eCollection 2021.
Finding concepts in large clinical ontologies can be challenging when queries use different vocabularies. A search algorithm that overcomes this problem is useful in applications such as concept normalisation and ontology matching, where concepts can be referred to in different ways, using different synonyms. In this paper, we present a deep learning based approach to build a semantic search system for large clinical ontologies. We propose a Triplet-BERT model and a method that generates training data directly from the ontologies. The model is evaluated using five real benchmark data sets and the results show that our approach achieves high results on both free text to concept and concept to concept searching tasks, and outperforms all baseline methods.
当查询使用不同词汇表时，在大型临床本体中查找概念可能具有挑战性。一种克服此问题的搜索算法在概念规范化和本体匹配等应用中非常有用，在这些应用中，概念可以使用不同的同义词以不同的方式引用。在本文中，我们提出了一种基于深度学习的方法来构建大型临床本体的语义搜索系统。我们提出了一种三胞胎-BERT 模型和一种直接从本体生成训练数据的方法。该模型使用五个真实基准数据集进行评估，结果表明，我们的方法在自由文本到概念和概念到概念搜索任务上都取得了很高的成绩，并且优于所有基线方法。
