LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts
Researchers have introduced LongMoE, a novel framework designed to tackle the complexities of multimodal clinical learning. This approach effectively addresses two key challenges: missing data across different patient modalities and the temporal dynamics of disease progression. By integrating context-aware imputation with trajectory-aware encoding and a sparse Mixture-of-Experts system, LongMoE can model disease evolution over time even with incomplete or inconsistent patient data. AI
IMPACT Establishes a new foundation for multimodal clinical learning by addressing data missingness and temporal dynamics.