[2510.17211] Temporally Detailed Hypergraph Neural ODEs for Disease Progression Modeling

Computer Science > Artificial Intelligence arXiv:2510.17211 (cs) [Submitted on 20 Oct 2025 (v1), last revised 29 Mar 2026 (this version, v2)] Title:Temporally Detailed Hypergraph Neural ODEs for Disease Progression Modeling Authors:Tingsong Xiao, Yao An Lee, Zelin Xu, Yupu Zhang, Zibo Liu, Yu Huang, Jiang Bian, Jingchuan Guo, Zhe Jiang View a PDF of the paper titled Temporally Detailed Hypergraph Neural ODEs for Disease Progression Modeling, by Tingsong Xiao and 8 other authors View PDF HTML (experimental) 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 continu...