Multi-Scale Spatiotemporal Dynamic Graph Neural Network for Early Prediction of Mortality Risks in Heart Failure Patients

Heart Failure (HF) stands as a principal public health issue worldwide, imposing a significant burden on healthcare systems. While existing prognostic methods have achieved certain milestones in predicting the early mortality risk of HF patients, they have not fully considered the dynamic interdependencies among physiological parameters. This paper introduces a novel Multi-scale Spatiotemporal Dynamic Graph Neural Network, MSTD-GNN, which enhances the prediction capability for early mortality in HF patients by dynamically extracting spatio-temporal information of physiological parameters from ICU patient Electronic Health Records (EHRs). Our model constructs dynamic graphs to model multivariate time series data, revealing the implicit dependencies between physiological parameters and capturing the inherent dynamics of the data. We conducted experiments using the MIMIC-III and MIMIC-IV datasets. The experimental results show that, compared to existing methods, MSTD-GNN demonstrates superior performance in predicting the early mortality risk of HF patients. On the MIMIC-III and MIMIC-IV datasets, the AUC scores of MSTD-GNN reached 83.93% and 81.74%, respectively. Furthermore, through dynamic graphs, our model unveils the dynamic relationships between physiological variables across different time scales.