Learning Preference-Based Objectives from Clinical Narratives for Sequential Treatment Decision-Making

Abstract

Designing reward functions remains a central challenge in reinforcement learning (RL) for healthcare, where outcomes are sparse, delayed, and difficult to specify. While structured data capture physiological states, they often fail to reflect the overall quality of a patient's clinical trajectory, including recovery dynamics, treatment burden, and stability. Clinical narratives, in contrast, summarize longitudinal reasoning and implicitly encode evaluations of treatment effectiveness. We propose Clinical Narrative-informed Preference Rewards (CN-PR), a framework for learning reward functions directly from discharge summaries by treating them as scalable supervision for trajectory-level preferences. Using a large language model, we derive trajectory quality scores (TQS) and construct pairwise preferences over patient trajectories, enabling reward learning via a structured preference-based objective. To account for variability in narrative informativeness, we incorporate a confidence signal that weights supervision based on its relevance to the decision-making task. The learned reward aligns strongly with trajectory quality (Spearman rho = 0.63) and enables policies that are consistently associated with improved recovery-related outcomes, including increased organ support-free days and faster shock resolution, while maintaining comparable performance on mortality. These effects persist under external validation. Our results demonstrate that narrative-derived supervision provides a scalable and expressive alternative to handcrafted or outcome-based reward design for dynamic treatment regimes.