Medical Entity Disambiguation Using Graph Neural Networks

TL;DR

This paper presents ED-GNN, a graph neural network-based approach for medical entity disambiguation that leverages query graphs and hard negative sampling to improve accuracy over existing methods.

Contribution

Introduction of ED-GNN, a novel GNN-based framework with optimization techniques for enhanced medical entity disambiguation performance.

Findings

Achieved 7.3% higher F1 score on five datasets compared to state-of-the-art.

Utilized query graph representation for entities in text snippets.

Implemented hard negative sampling to boost disambiguation accuracy.

Abstract

Medical knowledge bases (KBs), distilled from biomedical literature and regulatory actions, are expected to provide high-quality information to facilitate clinical decision making. Entity disambiguation (also referred to as entity linking) is considered as an essential task in unlocking the wealth of such medical KBs. However, existing medical entity disambiguation methods are not adequate due to word discrepancies between the entities in the KB and the text snippets in the source documents. Recently, graph neural networks (GNNs) have proven to be very effective and provide state-of-the-art results for many real-world applications with graph-structured data. In this paper, we introduce ED-GNN based on three representative GNNs (GraphSAGE, R-GCN, and MAGNN) for medical entity disambiguation. We develop two optimization techniques to fine-tune and improve ED-GNN. First, we introduce a novel strategy to represent entities that are mentioned in text snippets as a query graph. Second, we design an effective negative sampling strategy that identifies hard negative samples to improve the model's disambiguation capability. Compared to the best performing state-of-the-art solutions, our ED-GNN offers an average improvement of 7.3% in terms of F1 score on five real-world datasets.