Companion Animal Disease Diagnostics Based on Literal-Aware Medical Knowledge Graph Representation Learning

Abstract

Knowledge graph (KG) embedding has been used to benefit the diagnosis of animal diseases by analyzing electronic medical records (EMRs), such as notes and veterinary records. However, learning representations to capture entities and relations with literal information in KGs is challenging as the KGs show heterogeneous properties and various types of literal information. Meanwhile, the existing methods mostly aim to preserve graph structures surrounding target nodes without considering different types of literals, which could also carry significant information. In this paper, we propose a knowledge graph embedding model for the efficient diagnosis of animal diseases, which could learn various types of literal information and graph structure and fuse them into unified representations, namely LiteralKG. Specifically, we construct a knowledge graph that is built from EMRs along with literal information collected from various animal hospitals. We then fuse different types of entities and node feature information into unified vector representations through gate networks. Finally, we propose a self-supervised learning task to learn graph structure in pretext tasks and then towards various downstream tasks. Experimental results on link prediction tasks demonstrate that our model outperforms the baselines that consist of state-of-the-art models.

Predicting the daily number of patients for allergic diseases using PM10 concentration based on spatiotemporal graph convolutional networks

“…However, the population of patients with allergic diseases in each region exhibits irregularly structured data, similar to gene interaction networks and chemical molecular structures, which means that they are graph-structured data rather than regular grid data to which CNNs can be applied. Recently, graph convolutional networks (GCN) [ 35 – 37 ] have been used to capture the structural features of graphs for various tasks, including medical diagnoses. A few studies [ 38 ] have conducted disease prediction using the GCN model, which considers comorbidity relationships among diseases, to discover new disease correlations among their study cohorts.…”