PathwayLLM: Explainable Clinical Trajectory Modeling with Structured Pathways for Sepsis Prediction

Abstract

Patient-level sepsis prediction in the ICU requires models that track how a patient’s condition evolves over time and integrate heterogeneous structured evidence from electronic health records. We present PathwayLLM, a trajectory-based framework that grounds prediction on temporal signals together with graph-structured and pathway-level clinical information derived from statistical dependency discovery. PathwayLLM follows a three-stage design. First, each observation window is encoded from multiple structured views, including physiological measurements, temporal dynamics, a heterogeneous patient–diagnosis–medication graph, and pathway signals constructed from discovered conditional independence structures among clinical variables. Second, these representations are provided to a pre-trained language model as auxiliary contextual embeddings so that risk prediction and evidence-conditioned text explanations can be learned jointly. Third, a Clinical Trajectory LSTM with Deterioration Attention aggregates window-level representations to highlight critical deterioration points and produce a patient-level risk score. On MIMIC-IV (15,410 ICU patients; 8.45% sepsis prevalence), PathwayLLM achieves AUROC 0.891 and AUPRC 0.724, outperforming strong time-series and pre-trained baselines. Ablation studies indicate that trajectory aggregation and structured clinical signals are key contributors, and clinician review suggests that the generated explanations are coherent, interpretable, and clinically relevant.