Semantic Search for Large Scale Clinical Ontologies

TL;DR

This paper introduces a deep learning-based semantic search system for large clinical ontologies, improving concept retrieval across different vocabularies and outperforming baseline methods in benchmark tests.

Contribution

It presents a novel Triplet-BERT model and a data generation method directly from ontologies for enhanced semantic search in clinical domains.

Findings

High accuracy on free text to concept search

Superior performance in concept to concept matching

Effective across multiple benchmark datasets

Abstract

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.