MARC: Multi-Scale Adaptive Relation Calibration for Longitudinal EHR-based Heart Failure Prediction

Predicting whether heart failure is recorded at a subsequent visit from longitudinal electronic health records (EHRs) requires modeling both cross-visit dependencies and the relations among diagnosis codes. Existing methods exploit temporal information, medical knowledge, and graph structure, yet provide limited control over how these relations are integrated into code representations, graph propagation, and training supervision in a specific prediction context. We propose MARC, an adaptive relation-calibration framework built on the principle of preserving support while calibrating strength. MARC comprises three calibration mechanisms: knowledge-aware hierarchical semantic calibration retains code-specific information; state-aware edge calibration controls the propagation strength of observed co-occurrence edges; and class-aware patient relation calibration regularizes within-batch patient relations during training. Evaluated on heart-failure prediction cohorts constructed from MIMIC-III and MIMIC-IV, MARC achieves the highest mean AUROC, F1, and AUPRC and the lowest mean Brier score on both cohorts. A complete factorial study further reveals that the contributions of the three calibration mechanisms depend on the dataset and module combination, rather than combining as fixed additive gains. The source code is available at https://anonymous.4open.science/r/marc-reproducibility-5109/.