通过反事实数据增强和大语言模型改进罕见药物推荐
Improving Rare Medication Recommendation with Counterfactual Data Augmentation and Large Language Models
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中文总结 AI 辅助
针对现有方法在推荐罕见药物时性能低的问题，提出GenRxR框架，利用大语言模型生成反事实数据缓解数据稀缺，集成大语言模型建模联合推荐药物关系，经指令调整增强临床推理，实验显示其性能优于多个基线。
中文摘要 AI 辅助
基于人工智能的药物推荐系统因能提高患者安全性和治疗效果而备受关注。然而，现有方法在推荐罕见药物时预测性能较低。我们将此归因于两个内在局限：罕见药物数据稀缺和对联合推荐药物考虑有限。为此，我们提出了GenRxR框架，利用大语言模型生成反事实医疗数据，缓解数据稀缺问题，并将大语言模型集成到推荐过程中以建模联合推荐药物之间的关系。我们还引入指令调整步骤以增强临床推理。实验表明，GenRxR在多数情况下优于14个基线模型，对罕见药物的预测性能比最强基线高出30.9%。
英文摘要
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 capability of LLMs to generate counterfactual medical data, mitigating the data scarcity issue for rare-meds. It also integrates an LLM into the medication recommendation process to model relationships among co-recommended medications. To further enhance the clinical reasoning, we introduce an instruction tuning step that aligns the LLM's capability with the recommendation task, enabling better handling of clinical context, including rare-meds cases. In our experiments, we show that GenRxR outperforms 14 (including 5 LLM-based) baselines in most cases. Specifically, it achieves up to 30.9% higher predictive performance for rare-meds than the strongest baseline.
发表机构
- KAIST（韩国科学技术院）
- Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。
