CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914997362229248 |
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| author | Liang, Shunpan Li, Xiang Mu, Shi Li, Chen Lei, Yu Hou, Yulei Ma, Tengfei |
| author_facet | Liang, Shunpan Li, Xiang Mu, Shi Li, Chen Lei, Yu Hou, Yulei Ma, Tengfei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00880 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning Liang, Shunpan Li, Xiang Mu, Shi Li, Chen Lei, Yu Hou, Yulei Ma, Tengfei Information Retrieval Artificial Intelligence 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. |
| title | CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2403.00880 |