Knowledge-Based Spatio-Temporal Attention Network for Disease Prediction with Chinese Electronic Medical Records

The use of electronic medical record (EMR) to develop automated disease prediction has been suffering from sparse features. Graph neural network (GNN) can model the non-grid data and infer missing features, which is an effective method to solve the above problem. However, the relation of two entity nodes is difficult to explored by GNN, if they cross more than three sentences, which may loss some semantic information for disease inferring. Furthermore, temporal dependency of nodes in EMRs not only provides more information for predicting disease, but also enhances ability of semantic discrimination of node representations. This study proposes a knowledge-based spatio-temporal attention network (KSAN) for disease prediction. Knowledge graph and relation completion are firstly leveraged to build more relations among nodes to supply potential disease-related facts. Secondly, the dependency among nodes are comprehensively aggregated from spatio-temporal multidimension. Meanwhile, the key nodes' weights are increased by matching a key factor set, which provides more goal-directed information. Finally, the representative embedding is generated for disease prediction. The training of KSAN is based on semi-supervised learning, which helps to save resources. KSAN is evaluated using a real-world Chinese EMR dataset, the results indicate that KSAN achieves the best prediction performance.