M2M-FSurv: Multimodal Deep Functional Modeling for Competing Risks Survival Analysis

Modern clinical datasets contain rich multimodal information, including clinical narratives, longitudinal physiological measurements, and baseline patient characteristics. At the same time, patients may experience multiple competing outcomes, such as discharge or in-hospital death. Integrating these heterogeneous modalities to model competing events over time remains a fundamental challenge in survival analysis. Existing classical and deep learning–based survival models remain limited in jointly modeling multimodal inputs, competing risks, and interpretable continuous-time event predictions. To address this limitation, we propose M2M-FSurv, a deep learning framework for multimodal competing-risks survival analysis that incorporates principles from functional data analysis to estimate smooth, continuous-time cause-specific density incidence functions. M2M-FSurv employs an attention-based shared encoder that integrates inputs and adjusts modality contributions across individuals, along with cause-specific prediction heads that estimate smooth, cause-specific event-time densities approximated using B-spline expansions. We evaluate the framework on the MIMIC-III and METABRIC datasets. Experimental results demonstrate that M2M-FSurv achieves improved predictive performance while providing interpretable insights into modality contributions and cause-specific event dynamics.