The recommendation of medication is a vital aspect of intelligent healthcare systems, as it involves prescribing the most suitable drugs based on a patient's specific health needs. Unfortunately, many sophisticated models currently in use tend to overlook the nuanced semantics of medical data, while only relying heavily on identities. Furthermore, these models face significant challenges in handling cases involving patients who are visiting the hospital for the first time, as they lack prior prescription histories to draw upon. To tackle these issues, we harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs). Our research aims to transform existing medication recommendation methodologies using LLMs. In this paper, we introduce a novel approach called Large Language Model Distilling Medication Recommendation (LEADER). We begin by creating appropriate prompt templates that enable LLMs to suggest medications effectively. However, the straightforward integration of LLMs into recommender systems leads to an out-of-corpus issue specific to drugs. We handle it by adapting the LLMs with a novel output layer and a refined tuning loss function. Although LLM-based models exhibit remarkable capabilities, they are plagued by high computational costs during inference, which is impractical for the healthcare sector. To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model. Extensive experiments conducted on two real-world datasets, MIMIC-III and MIMIC-IV, demonstrate that our proposed model not only delivers effective results but also is efficient. To ease the reproducibility of our experiments, we release the implementation code online.

翻译：用药推荐是智能医疗系统中的关键环节，其核心在于根据患者特定的健康需求开具最合适的药物。然而，当前许多复杂的模型往往忽视医学数据的细微语义，仅过度依赖标识信息。此外，这些模型在处理初诊患者（缺乏既往用药史）时面临重大挑战。为解决这些问题，我们利用大型语言模型强大的语义理解能力和输入无关特性，旨在通过LLMs革新现有用药推荐方法。本文提出一种名为"大型语言模型蒸馏用药推荐"的新方法。我们首先设计合适的提示模板，使LLMs能够有效进行药物推荐。但将LLMs直接集成到推荐系统中会导致药物特有的语料外问题。我们通过引入新型输出层和优化微调损失函数对LLMs进行调整加以解决。尽管基于LLMs的模型展现出卓越能力，但推理过程中高昂的计算成本使其在医疗领域难以实际应用。为此，我们开发了一种特征级知识蒸馏技术，将LLMs的能力迁移至更紧凑的模型。在MIMIC-III和MIMIC-IV两个真实世界数据集上的大量实验表明，本文模型既能保证推荐效果，又能提升运行效率。为便于实验复现，我们已在线上公开实现代码。