As medical demands grow and machine learning technology advances, AI-based diagnostic and treatment systems are garnering increasing attention. Medication recommendation aims to integrate patients' long-term health records with medical knowledge, recommending accuracy and safe medication combinations for specific conditions. However, most existing researches treat medication recommendation systems merely as variants of traditional recommendation systems, overlooking the heterogeneity between medications and diseases. To address this challenge, we propose DGMed, a framework for medication recommendation. DGMed utilizes causal inference to uncover the connections among medical entities and presents an innovative feature alignment method to tackle heterogeneity issues. Specifically, this study first applies causal inference to analyze the quantified therapeutic effects of medications on specific diseases from historical records, uncovering potential links between medical entities. Subsequently, we integrate molecular-level knowledge, aligning the embeddings of medications and diseases within the molecular space to effectively tackle their heterogeneity. Ultimately, based on relationships at the entity level, we adaptively adjust the recommendation probabilities of medication and recommend medication combinations according to the patient's current health condition. Experimental results on a real-world dataset show that our method surpasses existing state-of-the-art baselines in four evaluation metrics, demonstrating superior performance in both accuracy and safety aspects. Compared to the sub-optimal model, our approach improved accuracy by 4.40%, reduced the risk of side effects by 6.14%, and increased time efficiency by 47.15%.

翻译：随着医疗需求的增长与机器学习技术的进步，基于人工智能的诊断与治疗系统日益受到关注。药物推荐旨在整合患者的长期健康记录与医学知识，针对特定疾病推荐精准且安全的药物组合。然而，现有研究大多将药物推荐系统视为传统推荐系统的变体，忽视了药物与疾病之间的异质性。为解决这一挑战，我们提出DGMed——一个药物推荐框架。DGMed利用因果推断揭示医学实体间的关联，并引入创新的特征对齐方法以应对异质性问题。具体而言，本研究首次应用因果推断从历史记录中分析药物对特定疾病的量化治疗效果，揭示医学实体间的潜在联系；随后，整合分子层面知识，在分子空间中对齐药物与疾病的嵌入表示，有效处理其异质性；最终，基于实体层面的关系，根据患者当前健康状况自适应调整药物推荐概率并推荐药物组合。在真实数据集上的实验结果表明，我们的方法在四项评估指标上均超越现有的最优基线模型，在准确性与安全性方面均展现出卓越性能。与次优模型相比，本方法准确性提升4.40%，副作用风险降低6.14%，时间效率提高47.15%。