Fine-grained List-wise Alignment for Generative Medication Recommendation
Journal:
arXiv
Published Date:
May 26, 2025
Abstract
Accurate and safe medication recommendations are critical for effective
clinical decision-making, especially in multimorbidity cases. However, existing
systems rely on point-wise prediction paradigms that overlook synergistic drug
effects and potential adverse drug-drug interactions (DDIs). We propose FLAME,
a fine-grained list-wise alignment framework for large language models (LLMs),
enabling drug-by-drug generation of drug lists. FLAME formulates recommendation
as a sequential decision process, where each step adds or removes a single
drug. To provide fine-grained learning signals, we devise step-wise Group
Relative Policy Optimization (GRPO) with potential-based reward shaping, which
explicitly models DDIs and optimizes the contribution of each drug to the
overall prescription. Furthermore, FLAME enhances patient modeling by
integrating structured clinical knowledge and collaborative information into
the representation space of LLMs. Experiments on benchmark datasets demonstrate
that FLAME achieves state-of-the-art performance, delivering superior accuracy,
controllable safety-accuracy trade-offs, and strong generalization across
diverse clinical scenarios. Our code is available at
https://github.com/cxfann/Flame.
