Graph-based structural knowledge-aware network for diagnosis assistant

Abstract

Diagnosis assistant is an effective way to reduce the workloads of professional doctors. The rich professional knowledge plays a crucial role in diagnosis. Therefore, it is important to introduce the relevant medical knowledge into diagnosis assistant. In this paper, diagnosis assistant is treated as a classification task, and a Graph-based Structural Knowledge-aware Network (GSKN) model is proposed to fuse Electronic Medical Records (EMRs) and medical knowledge graph. Considering that different information in EMRs affects the diagnosis results differently, the information in EMRs is categorized into general information, key information and numerical information, and is introduced to GSKN by adding an enhancement layer to the Bidirectional Encoder Representation from Transformers (BERT) model. The entities in EMRs are recognized, and Graph Convolutional Neural Networks (GCN) is employed to learn deep-level graph structure information and dynamic representation of these entities in the subgraphs. An interactive attention mechanism is utilized to fuse the enhanced textual representation and the deep representation of these subgraphs. Experimental results on Chinese Obstetric Electronic Medical Records (COEMRs) and open dataset C-EMRs demonstrate the effectiveness of our model.

Keywords

• electronic medical records
• diagnosis assistant
• knowledge graph
• multi-label classification