Multimodal deep learning for clinical decision support frequently fails in real-world deployments due to severe data missingness and sensor noise. Standard fusion and Mixture-of-Experts (MoE) architectures assume all modalities are uniformly informative, causing routing collapse and miscalibrated confidence when faced with corrupted inputs. My doctoral research addresses this fragility by shifting the paradigm from generative imputation to uncertainty-guided abstention. As a foundational step, I introduce QA-MoE (Quality-Aware MoE), which explicitly decouples reliability estimation from the routing process. By quantifying epistemic uncertainty, the model dynamically filters noisy modalities before fusion, ensuring sparse and stable inference. Building on this static robustness, my ongoing and future research extends to longitudinal and structured clinical data. I am developing PathFlow-EM, which integrates Expectation-Maximization to handle hidden variable distributions in unevenly sampled time-series, and Uncertainty-Guided Dynamic Topological Fusion (U-DTF), which uses topological data analysis to gate features based on evolving patient graphs. Ultimately, this research trajectory aims to deliver robust, calibration-aware architectures that form the computational basis for reliable Patient Digital Twins.