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：随着有限医疗资源与日益增长需求之间的失衡加剧，基于人工智能的临床任务变得至关重要。药物推荐作为其子领域，旨在融合纵向患者病史与医学知识，辅助医生开具更安全、更精准的药物组合。现有方法忽视了医疗数据中固有的长尾分布，未能实现头部数据与尾部数据间的均衡表征，导致模型性能次优。为解决这一挑战，我们提出StratMed模型，该模型引入了一种创新的关联分层机制。它能协调数据长尾分布中的差异，并平衡药物组合的安全性与准确性。具体而言，我们首先构建基于深度学习网络的预训练方法以获取实体表征。随后，设计金字塔式的数据分层方法，通过强化罕见实体特征来获得更具泛化性的实体关系。基于此关系，我们设计了两种图结构以在同一层面表达药物精准性与安全性，从而获取就诊表征。最后，拟合患者历史临床信息，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）上均超越了当前最先进方法。