Dual-Granularity Medication Recommendation Based on Causal Inference

• URL: http://arxiv.org/abs/2403.00880v1
• Date: Fri, 1 Mar 2024 08:50:27 GMT
• Title: Dual-Granularity Medication Recommendation Based on Causal Inference
• Authors: Shunpan Liang, Xiang Li, Xiang Li, Chen Li, Yu Lei, Yulei Hou, Tengfei Ma
• Abstract summary: Medication recommendation aims to integrate patients' long-term health records with medical knowledge. Most existing researches treat medication recommendation systems merely as variants of traditional recommendation systems. We propose DGMed, a framework for medication recommendation.
• Score: 14.814729935085635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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