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%.

提出了 DGMed，一种应用因果推断解决药物推荐系统中异质性问题的框架，通过分析历史记录中药物对特定疾病的量化治疗效果，揭示医学实体之间的潜在联系，利用分子层次的知识进行特征对齐，最终根据患者的当前健康状况自适应调整药物推荐概率并推荐药物组合，实验证明该方法在准确性、安全性和时间效率方面优于现有的基线模型。