用历史病例增强大模型，提升药物相互作用预测准确率
Case-Based Reasoning Enhances the Predictive Power of LLMs in Drug-Drug Interaction
- 从历史病例中提取药理知识，构建可检索的推理库
- 相比主流大模型，准确率提升28.7%
- 适合需要高可解释性的医药智能决策场景
药物相互作用（DDI）预测对治疗安全至关重要。尽管大语言模型（LLMs）在制药任务中展现出潜力，但其在DDI预测上的表现仍面临挑战。受临床实践中医生常参考相似历史病例进行决策的启发，我们提出CBR-DDI框架，通过案例推理（CBR）机制，将历史病例中的药理原理提炼为知识，以增强LLM在DDI任务中的推理能力。CBR-DDI利用大语言模型提取药理洞察，并结合图神经网络（GNNs）建模药物关联，构建知识库；采用混合检索机制与双层知识增强提示策略，使LLM能有效检索并复用相关病例；同时引入代表性采样策略实现动态案例优化。大量实验表明，CBR-DDI达到当前最优性能，相比主流LLMs及CBR基线，准确率提升28.7%，且保持高可解释性与灵活性。
原文摘要 · Abstract (English)
Drug-drug interaction (DDI) prediction is critical for treatment safety. While large language models (LLMs) show promise in pharmaceutical tasks, their effectiveness in DDI prediction remains challenging. Inspired by the well-established clinical practice where physicians routinely reference similar historical cases to guide their decisions through case-based reasoning (CBR), we propose CBR-DDI, a novel framework that distills pharmacological principles from historical cases to improve LLM reasoning for DDI tasks. CBR-DDI constructs a knowledge repository by leveraging LLMs to extract pharmacological insights and graph neural networks (GNNs) to model drug associations. A hybrid retrieval mechanism and dual-layer knowledge-enhanced prompting allow LLMs to effectively retrieve and reuse relevant cases. We further introduce a representative sampling strategy for dynamic case refinement. Extensive experiments demonstrate that CBR-DDI achieves state-of-the-art performance, with a significant 28.7% accuracy improvement over both popular LLMs and CBR baseline, while maintaining high interpretability and flexibility.
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