TAGNet: Temporal Aware Graph Convolution Network for Clinical Information Extraction

In medical informatics, the most common task for data mining on electronic medical records (EMR) is disease auxiliary diagnosis, which predicts the health statuses of patients according to their historical EMRs. With the popularity of deep representation learning, researchers use deep learning-based methods to learn an effective representation from EMR data for dealing with target clinical prediction tasks. However, few work comes down to the underlying structures of EMR data, which is obvious and important in clinical practice. Therefore, this paper proposes a new method, called TAGNet, to learn a robust representation by utilizing both the structural information and the temporal information from EMR data. Verified by experiments, TAGNet achieves state-of-the-art performance on the real-world EMR dataset.