Hierarchical Relational Trajectory Modeling for Longitudinal Clinical Prognosis

Prognosis from longitudinal electronic health records requires integrating evidence that is generated across hospitalizations and days, yet most existing models do not align their modeling of such records with this process, so a prediction cannot be traced back to the evidence it rests on, and whether a model actually relies on the trajectory cannot be checked. To address this mismatch, we propose **HiReT**, a hierarchical relational trajectory model that not only reconstructs patient records into a `visit-day-node` hierarchy, but also explicitly models the dynamic dependencies among heterogeneous evidence. **HiReT** expresses the dependencies among evidence types with a typed relation map within each visit, and uses hierarchical aggregation to follow the clinical data flow, combining days into visits and visits into a patient representation. Experiments on a natural-distribution six-class mortality benchmark from MIMIC-IV show that **HiReT** achieves relative improvements of 19.95%, 27.81%, and 47.39% in Balanced Accuracy, Macro-F1, and quadratic weighted kappa (QWK), respectively, over the strongest baseline, and is the best performer on all five metrics of a prolonged length-of-stay (LOS) benchmark. Furthermore, ablations over the model's structure and evidence types demonstrate that the model's predictions are genuinely grounded in specific clinical evidence. Overall, these results indicate that aligning a model with how clinical evidence is generated improves prognosis accuracy and makes the basis of its predictions checkable.