Improving Rare Medication Recommendation with Counterfactual Data Augmentation and Large Language Models
AI-based medication recommendation systems have attracted substantial attention due to their potential to enhance patient safety and therapeutic outcomes. Despite the clinical importance of accurately recommending rarely prescribed medications (rare-meds), we observe that most existing methods show significantly lower predictive performance for rare-meds. We attribute this issue to two intrinsic limitations: (a) the inherent scarcity of data for rare-meds and (b) limited consideration of co-recommended medications. To address these limitations, we propose GenRxR, a novel framework based on large language models (LLMs). GenRxR leverages the medical knowledge and clinical reasoning capabili...
| Authors | Shinhwan Kang, Soo Yong Lee, Jaewon Kim et al. |
|---|---|
| Primary category | cs.IR |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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