用轻量微调+代码提示，把医生问诊对话转成结构化病历。
EMRModel: A Large Language Model for Extracting Medical Consultation Dialogues into Structured Medical Records
- 结合LoRA微调与代码风格提示，提升病历结构化提取效率。
- 在真实对话数据上达到88.1%的F1值，比通用模型高49.5%。
- 专为医疗对话设计评估基准，适合临床NLP研究者参考。
医生问诊对话包含关键临床信息，但其非结构化特性限制了在诊疗中的有效利用。传统基于规则或浅层机器学习的方法难以捕捉深层隐含语义。近期，大预训练语言模型与低秩适应（LoRA）这一轻量级微调方法展现出在结构化信息抽取中的潜力。我们提出EMRModel，通过融合LoRA微调与代码风格提示设计，旨在高效将医疗问诊对话转化为结构化电子病历（EMRs）。此外，我们构建了一个高质量、真实场景下的问诊对话数据集，并附有详细标注。同时，我们引入细粒度的问诊信息抽取评估基准，提供系统化评估方法，推动医疗自然语言处理模型优化。实验表明，EMRModel取得88.1%的F1分数，相比标准预训练模型提升49.5%。相较于传统LoRA微调方法，本模型表现更优，验证了其在结构化病历提取任务中的有效性。
原文摘要 · Abstract (English)
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.
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