Improving Heart-Failure Predictive Tasks with Patient Health Knowledge Graphs and Sequential Graph Neural Networks

Patient health knowledge graphs provide a means for high-quality and interoperable clinical data representation, while graph neural networks are a key enabler in order to learn their underlying relationships for downstream prediction tasks. In this work, we propose a sequential graph neural network (SeqGNN) framework that models patient visit trajectories for multiple binary clinical tasks, namely diagnosis, readmission, and mortality prediction. The proposed architecture integrates temporal dynamics with graph-based representations that enhances patient-level embeddings. Focusing on patients at the risk of Heart Failure (HF), our methodology achieves comparatively high accuracy and precision-versus-recall tradeoffs on highly heterogeneous graphs and imbalanced labeled data. We additionally quantify the uncertainty concerning each clinical decision making task and, compared with the state-of-the-art, show that AUROC and AUPRC scores reliably improve for onset diagnosis in particular, as high as 93.1 and 79.1 respectively. Our experiments conducted on real-world data from an intensive care unit demonstrate the potential for sequential representation learning over patient health knowledge graphs that can be provided for high-precision decision-making in clinical settings.