  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.
