TRIAGE: Dialectical LLM Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series
TRIAGE trains a Large Language Model to generate dialectical reasoning over competing clinical outcomes. By requiring the model to create separate, outcome-specific rationales for each possibility, it avoids 'risk polarization,' allowing a single model to produce continuous risk scores grounded in explicit clinical justification.