DAG-Biased Graph Learning for Multimodal Survival on EHR

Existing deep learning architectures for survival prediction from electronic health records (EHRs) largely assume unimodality, failing to capture the complex relational structure across diverse clinical modalities. Graph learning can allow us to robustly adapt to the underlying clinical structure of the data to have more predictive and interpretable representations for time-to-event analysis. However, unconstrained graph learning produces dense, bidirectional adjacencies that overfit. We introduce M-CADENCE, a multimodal survival model that integrates structured acyclic priors with attention-based propagation. Each modality has its own learnable adjacency, regularised toward acyclicity through a log-determinant characterisation and toward sparsity through soft thresholding. A cross-modal meta-adjacency keeps the cross-modal parameter count quadratic in the number of modalities rather than in their feature dimensions. Propagation is then performed by multi-head attention biased by the learnt structure. The propagation step degrades gracefully under sparse or misspecified structure, addressing a known weakness of hard-routing graph variants. We evaluate M-CADENCE on five EHR benchmarks covering intensive-care mortality, circulatory failure, and emergency-department competing risks, against multiple survival, graph, and structure learning baselines. M-CADENCE achieves the best concordance on all datasets, the best early-warning AUC on four of five, and competitive precision-recall against the strongest deep survival baselines. Removing the DAG bias lowers concordance, as does substitution with a density-matched random adjacency, which confirms that the specific learnt structure is required. Discrimination is stable across four orders of magnitude in regularisation strength, and evaluation by a panel of ten language models confirms M-CADENCE learns clinical edges that are more physiologically plausible than baseline structure-learning methods.