Temporally Detailed Hypergraph Neural ODE for Disease Progression Modeling

Tingsong Xiao, Yao An Lee, Zelin Xu, Yupu Zhang, Zibo Liu, Yu Huang, Jiang Bian, Jingchuan Guo, Zhe Jiang

Keywords: Disease Progression Modeling, Neural ODE, Temporally Detailed Hypergraph

Abstract:

Disease progression modeling aims to characterize and predict how a patient's disease complications worsen over time based on longitudinal electronic health records (EHRs). For diseases such as type 2 diabetes, accurate progression modeling can enhance patient sub-phenotyping and inform effective and timely interventions. However, the problem is challenging due to the need to learn continuous-time progression dynamics from irregularly sampled clinical events amid patient heterogeneity (e.g., different progression rates and pathways).
Existing mechanistic and data-driven methods either lack adaptability to learn from real-world data or fail to capture complex continuous-time dynamics on progression trajectories. To address these limitations, we propose Temporally Detailed Hypergraph Neural Ordinary Differential Equation (TD-HNODE), which represents disease progression on clinically recognized trajectories as a temporally detailed hypergraph and learns the continuous-time progression dynamics via a neural ODE framework. TD-HNODE contains a learnable TD-Hypergraph Laplacian that captures the interdependency of disease complication markers within both intra- and inter-progression trajectories. Experiments on two real-world clinical datasets demonstrate that TD-HNODE outperforms multiple baselines in modeling the progression of type 2 diabetes and related cardiovascular diseases.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 6307