Multimodal Disease Progression Modeling via Spatiotemporal Disentanglement and Multiscale Alignment

• URL: http://arxiv.org/abs/2510.11112v1
• Date: Mon, 13 Oct 2025 08:02:36 GMT
• Title: Multimodal Disease Progression Modeling via Spatiotemporal Disentanglement and Multiscale Alignment
• Authors: Chen Liu, Wenfang Yao, Kejing Yin, William K. Cheung, Jing Qin,
• Abstract summary: $textttDiPro$ is a novel framework that addresses challenges through region-aware disentanglement and multi-timescale alignment.<n>Experiments on the MIMIC dataset demonstrate that $textttDiPro$ could effectively extract temporal clinical dynamics.
• Score: 16.824692012617334
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Longitudinal multimodal data, including electronic health records (EHR) and sequential chest X-rays (CXRs), is critical for modeling disease progression, yet remains underutilized due to two key challenges: (1) redundancy in consecutive CXR sequences, where static anatomical regions dominate over clinically-meaningful dynamics, and (2) temporal misalignment between sparse, irregular imaging and continuous EHR data. We introduce $\texttt{DiPro}$, a novel framework that addresses these challenges through region-aware disentanglement and multi-timescale alignment. First, we disentangle static (anatomy) and dynamic (pathology progression) features in sequential CXRs, prioritizing disease-relevant changes. Second, we hierarchically align these static and dynamic CXR features with asynchronous EHR data via local (pairwise interval-level) and global (full-sequence) synchronization to model coherent progression pathways. Extensive experiments on the MIMIC dataset demonstrate that $\texttt{DiPro}$ could effectively extract temporal clinical dynamics and achieve state-of-the-art performance on both disease progression identification and general ICU prediction tasks.

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