Semantic Search for Large Scale Clinical Ontologies
This addresses a problem in healthcare informatics for concept normalization and ontology matching, but it is incremental as it builds on existing deep learning methods.
The paper tackles the challenge of finding concepts in large clinical ontologies when queries use different vocabularies, and presents a deep learning-based semantic search system that achieves high results and outperforms all baseline methods on five real benchmark datasets.
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.