  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%.
