CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning

• URL: http://arxiv.org/abs/2403.00880v2
• Date: Wed, 30 Oct 2024 05:18:03 GMT
• Title: CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning
• Authors: Shunpan Liang, Xiang Li, Shi Mu, Chen Li, Yu Lei, Yulei Hou, Tengfei Ma,
• Abstract summary: Medication recommendation aims to integrate patients' long-term health records to provide accurate and safe medication combinations. Existing methods often fail to deeply explore the true causal relationships between diseases/procedures and medications. We propose the Causal Inference-driven Dual-Granularity Medication Recommendation method (CIDGMed)
• Score: 10.60553153370577
• License:
• Abstract: Medication recommendation aims to integrate patients' long-term health records to provide accurate and safe medication combinations for specific health states. Existing methods often fail to deeply explore the true causal relationships between diseases/procedures and medications, resulting in biased recommendations. Additionally, in medication representation learning, the relationships between information at different granularities of medications, coarse-grained (medication itself) and fine-grained (molecular level), are not effectively integrated, leading to biases in representation learning. To address these limitations, we propose the Causal Inference-driven Dual-Granularity Medication Recommendation method (CIDGMed). Our approach leverages causal inference to uncover the relationships between diseases/procedures and medications, thereby enhancing the rationality and interpretability of recommendations. By integrating coarse-grained medication effects with fine-grained molecular structure information, CIDGMed provides a comprehensive representation of medications. Additionally, we employ a bias correction model during the prediction phase to further refine recommendations, ensuring both accuracy and safety. Through extensive experiments, CIDGMed significantly outperforms current state-of-the-art models across multiple metrics, achieving a 2.54% increase in accuracy, a 3.65% reduction in side effects, and a 39.42% improvement in time efficiency. Additionally, we demonstrate the rationale of CIDGMed through a case study.

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