CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning
This addresses medication recommendation for patients by enhancing accuracy and safety, though it appears incremental as it builds on existing methods with specific improvements.
The paper tackled the problem of biased medication recommendations by proposing CIDGMed, which uses causal inference and dual-granularity learning to improve accuracy and safety, resulting in a 2.54% increase in accuracy, a 3.65% reduction in side effects, and a 39.42% improvement in time efficiency.
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.