Time-aware Context-Gated Graph Attention Network for Clinical Risk Prediction

Clinical risk prediction based on Electronic Health Records (EHR) can assist doctors in better judgment and can make sense of early diagnosis. However, the prediction performance heavily relies on effective representations from multi-dimensional time-series EHR data. Existing solutions usually focus on temporal features or inherent relations between clinical event variables or extract both information in two separate phases. This usually leads to insufficient patient feature information and results in poor prediction performance. Moreover, existing methods based on Heterogeneous Graph Neural Network usually require manual selection of proper Meta-Paths. To solve these problems, we propose the Time-aware Context-Gated Graph Attention Network (T-ContextGGAN). Specifically, we design a GNN based module with Time-aware Meta-Paths and self-attention mechanism to extract both temporal semantic information and inherent relations of EHR data simultaneously and perform automatic Meta-Path selection. To evaluate the proposed model, we extract the first 48 hour EHR data in the first Intensive Care Unit (ICU) admission of three different tasks from two open-source datasets and model various clinical variables on the proposed EHRGraph. Extensive experimental results show the proposed model can effectively extract informative features, and outperform existing state-of-art models in terms of various prediction measures. Our code is available in https://github.com/OwlCitizen/TContext-GGAN.