Medical consultation dialogues contain critical clinical information, yet their unstructured nature hinders effective utilization in diagnosis and treatment. Traditional methods, relying on rule-based or shallow machine learning techniques, struggle to capture deep and implicit semantics. Recently, large pre-trained language models and Low-Rank Adaptation (LoRA), a lightweight fine-tuning method, have shown promise for structured information extraction. We propose EMRModel, a novel approach that integrates LoRA-based fine-tuning with code-style prompt design, aiming to efficiently convert medical consultation dialogues into structured electronic medical records (EMRs). Additionally, we construct a high-quality, realistically grounded dataset of medical consultation dialogues with detailed annotations. Furthermore, we introduce a fine-grained evaluation benchmark for medical consultation information extraction and provide a systematic evaluation methodology, advancing the optimization of medical natural language processing (NLP) models. Experimental results show EMRModel achieves an F1 score of 88.1%, improving by49.5% over standard pre-trained models. Compared to traditional LoRA fine-tuning methods, our model shows superior performance, highlighting its effectiveness in structured medical record extraction tasks.

本研究针对医疗咨询对话中信息未能有效利用的问题，提出了一种新的解决方案EMRModel，通过结合基于LoRA的微调和代码风格的提示设计，有效地将对话转化为结构化电子医疗记录。实验结果显示，该模型在医疗记录提取任务中取得了88.1%的F1得分，较标准预训练模型提高了49.5%，验证了其在医疗自然语言处理中的显著效果。

EMRModel：用于将医疗咨询对话提取为结构化医疗记录的大型语言模型