Modeling Longitudinal Clinical Dynamics with Covariance-Aware Neural Kalman Filtering for Multimodal Survival Prediction

Cancer prognosis reflects both baseline molecular characteristics and clinical evolution during follow-up. We propose a covariance-aware neural Kalman framework that integrates baseline RNA-seq and demographic covariates with irregular longitudinal clinical observations. A neural observation encoder maps clinical visits into a latent state space, where explicit covariance propagation and an analytically computed Kalman gain govern sequential state updates. Elapsed-time-dependent process covariance incorporates irregular visit intervals into the correction mechanism. Independently trained molecular and clinical predictors are combined through risk-level fusion selected by inner cross-validation. We evaluate the framework on 556 patients with 95 observed events from the MMRF CoMMpass multiple myeloma cohort using a 365-day landmark design and five repeated nested cross validation partitions. RNA + Covariance Kalman achieves the highest mean pooled out-of-fold C-index among the evaluated models, 0.716 ± 0.013, compared with 0.662 ± 0.028 for the static molecular model and 0.702 ± 0.012 for RNA + GRU. Its one- and two-year post-landmark AUCs are 0.743 ± 0.019 and 0.740 ± 0.021, respectively, with a two-year Brier score of 0.121 ± 0.004. Ablations support the contributions of covariance-derived correction and elapsed-time-dependent process covariance. These results demonstrate the value of covariance-aware state estimation for integrating molecular risk with longitudinal clinical trajectories in this cohort.