With the growing imbalance between limited medical resources and escalating demands, AI-based clinical tasks have become paramount. Medication recommendation, as a sub-domain, aims to amalgamate longitudinal patient history with medical knowledge, assisting physicians in prescribing safer and more accurate medication combinations. Existing methods overlook the inherent long-tail distribution in medical data, lacking balanced representation between head and tail data, which leads to sub-optimal model performance. To address this challenge, we introduce StratMed, a model that incorporates an innovative relevance stratification mechanism. It harmonizes discrepancies in data long-tail distribution and strikes a balance between the safety and accuracy of medication combinations. Specifically, we first construct a pre-training method using deep learning networks to obtain entity representation. After that, we design a pyramid-like data stratification method to obtain more generalized entity relationships by reinforcing the features of unpopular entities. Based on this relationship, we designed two graph structures to express medication precision and safety at the same level to obtain visit representations. Finally, the patient's historical clinical information is fitted to generate medication combinations for the current health condition. Experiments on the MIMIC-III dataset demonstrate that our method has outperformed current state-of-the-art methods in four evaluation metrics (including safety and accuracy).

翻译：随着有限的医疗资源与日益增长的需求之间失衡加剧，基于人工智能的临床任务变得至关重要。用药推荐作为其子领域，旨在融合纵向患者病史与医学知识，协助医生开具更安全、更精准的药物组合。现有方法忽略了医疗数据中固有的长尾分布，缺乏对头部与尾部数据的均衡表示，导致模型性能欠优。针对这一挑战，我们提出StratMed模型，其引入了一种创新的相关性分层机制。该机制协调了数据长尾分布的差异，并在药物组合的安全性与准确性之间取得平衡。具体而言，我们首先利用深度学习网络构建预训练方法以获得实体表示。随后，设计了一种金字塔式数据分层方法，通过强化非流行实体的特征来获取更泛化的实体关系。基于此关系，我们设计了两种图结构，在同等层次上表达药物精确性与安全性，从而获得就诊表示。最后，拟合患者历史临床信息，以生成针对当前健康状况的药物组合。在MIMIC-III数据集上的实验表明，我们的方法在四个评估指标（包括安全性与准确性）上均优于当前最先进方法。