用大模型提升法语药方指令的结构化，兼顾准确与效率
Automatic Posology Structuration : What role for LLMs?
- 结合命名实体识别与微调大模型，实现药方指令结构化
- 混合方案达91%准确率，显著降低计算开销
- 适合临床决策支持系统快速落地
自动结构化用药指令对提升用药安全和临床决策支持至关重要。在法语处方中，这些指令常模糊、不规范或口语化，限制了传统机器学习方法的效果。本文探索使用大语言模型（LLMs）将自由文本的用药指令转化为结构化格式，对比提示工程与微调方法，并与基于命名实体识别与链接（NERL）的“前大模型”系统进行比较。结果显示，尽管提示工程能提升性能，但仅微调后的LLM能达到基线水平。通过错误分析发现，NERL在结构精度上更优，而LLM更能处理语义细节。据此提出混合流水线：将NERL置信度低于0.8的案例转给LLM，根据置信度选择输出。该策略在保持91%结构化准确率的同时，显著降低延迟与计算成本，为实际临床应用提供可扩展解决方案。
原文摘要 · Abstract (English)
Automatically structuring posology instructions is essential for improving medication safety and enabling clinical decision support. In French prescriptions, these instructions are often ambiguous, irregular, or colloquial, limiting the effectiveness of classic ML pipelines. We explore the use of Large Language Models (LLMs) to convert free-text posologies into structured formats, comparing prompt-based methods and fine-tuning against a "pre-LLM" system based on Named Entity Recognition and Linking (NERL). Our results show that while prompting improves performance, only fine-tuned LLMs match the accuracy of the baseline. Through error analysis, we observe complementary strengths: NERL offers structural precision, while LLMs better handle semantic nuances. Based on this, we propose a hybrid pipeline that routes low-confidence cases from NERL (<0.8) to the LLM, selecting outputs based on confidence scores. This strategy achieves 91% structuration accuracy while minimizing latency and compute. Our results show that this hybrid approach improves structuration accuracy while limiting computational cost, offering a scalable solution for real-world clinical use.
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