让大模型更懂用药，避免过度开药，提升推荐精准度。
Fine-grained Alignment of Large Language Models for General Medication Recommendation without Overprescription
- 用药物感知方式微调大模型，更好利用临床记录。
- 内部验证准确率超现有方法10%以上，跨时间/外部数据也有效。
- 对罕见药物也能保持高精度，适合临床实用场景。
大语言模型（LLMs）在通用药物推荐中展现巨大潜力，因其能全面理解临床笔记并灵活编码药物。我们评估了通用与医学专用的LLMs，发现其精度不足且存在严重过度开药问题。为此，我们提出语言辅助药物推荐框架，以药物感知方式定制LLMs，提升临床笔记利用率。该框架微调后的模型在内部验证中准确率超过现有方法10%，并在时间跨域与外部验证中表现良好。此外，模型在面对分布外药物时仍保持高精度。
原文摘要 · Abstract (English)
Large language models (LLMs) holds significant promise in achieving general medication recommendation systems owing to their comprehensive interpretation of clinical notes and flexibility to medication encoding. We evaluated both general-purpose and medical-specific LLMs for medication recommendations, showing their unsatisfactory precision and severe overprescription. To address this, we introduce Language-Assisted Medication Recommendation, which tailors LLMs for medication recommendation in a medication-aware manner, improving the usage of clinical notes. Fine-tuning LLMs with this framework can outperform existing methods by more than 10% in internal validation and generalize across temporal and external validations. Furthermore, the model maintains high accuracy when encountering out-of-distribution medication.
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