DisGraph-RP: Graph-Augmented Temporal Modeling with Aspect-Based Contrastive Encoding of Discharge Summary for Readmission Prediction

AbstractPredicting hospital readmissions is a critical clinical task with substantial implications for patient outcomes and healthcare cost management. We propose DisGraph-RP, a graph-augmented temporal modeling framework that integrates structured discourse-aware text representation with cross-admission relational reasoning. Our approach introduces a Section-Aware Contrastive Encoder that leverages section segmentation and aspect-based supervision to produce fine-grained representations of discharge summaries. These representations are then composed over time using a Graph-Based temporal module that encodes inter-visit dependencies through learned edge relations, enabling the model to capture disease progression, treatment history, and recurrent risk signals. Experiments on multiple real-world datasets demonstrate that DisGraph-RP achieves significant improvements over strong baselines, including transformer-based clinical models and prompting-based LLM approaches. Our findings highlight the importance of combining discourse-informed text encoding with temporal graph reasoning for robust clinical outcome prediction.