Temporally Detailed Hypergraph Neural ODEs for Type 2 Diabetes Progression Modeling

• URL: http://arxiv.org/abs/2510.17211v1
• Date: Mon, 20 Oct 2025 06:54:29 GMT
• Title: Temporally Detailed Hypergraph Neural ODEs for Type 2 Diabetes Progression Modeling
• Authors: Tingsong Xiao, Yao An Lee, Zelin Xu, Yupu Zhang, Zibo Liu, Yu Huang, Jiang Bian, Serena Jingchuan Guo, Zhe Jiang,
• Abstract summary: Disease progression modeling aims to characterize and predict how a patient's disease complications worsen over time.<n>The problem is challenging due to the need to learn continuous-time dynamics of progression patterns.<n>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.
• Score: 14.111509829509243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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). Accurate modeling of disease progression, such as type 2 diabetes, can enhance patient sub-phenotyping and inform effective and timely interventions. However, the problem is challenging due to the need to learn continuous-time dynamics of progression patterns based on irregular-time event samples and patient heterogeneity (\eg 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.

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