用大模型辅助构建临床规则系统，提升开发效率与透明度。
Initial Investigation of LLM-Assisted Development of Rule-Based Clinical NLP System
- 用大模型自动找病历片段和关键词，替代人工规则设计。
- 对关键文本片段召回率达0.98~0.99，关键词提取准确率1.0。
- 适合需要可解释性的医疗NLP系统快速搭建，如医院信息处理。
尽管机器学习和大语言模型（LLMs）取得进展，规则型自然语言处理（NLP）系统因可解释性和运行效率仍广泛用于临床场景。然而其手动开发与维护成本高，尤其在语言变体多的任务中。为此，我们提出一种新方法：仅在规则系统开发阶段使用大模型。实验聚焦开发流程的前两步：从临床记录中定位相关片段；从片段中提取用于命名实体识别（NER）的关键词。结果显示，采用Deepseek和Qwen的大模型在识别临床相关文本片段上召回率分别达0.98和0.99，关键词提取准确率为1.0。该研究揭示了大模型辅助规则系统开发的新方向，相比深度学习模型方案，能实现更快速、低成本且透明的系统构建。
原文摘要 · Abstract (English)
Despite advances in machine learning (ML) and large language models (LLMs), rule-based natural language processing (NLP) systems remain active in clinical settings due to their interpretability and operational efficiency. However, their manual development and maintenance are labor-intensive, particularly in tasks with large linguistic variability. To overcome these limitations, we proposed a novel approach employing LLMs solely during the rule-based systems development phase. We conducted the initial experiments focusing on the first two steps of developing a rule-based NLP pipeline: find relevant snippets from the clinical note; extract informative keywords from the snippets for the rule-based named entity recognition (NER) component. Our experiments demonstrated exceptional recall in identifying clinically relevant text snippets (Deepseek: 0.98, Qwen: 0.99) and 1.0 in extracting key terms for NER. This study sheds light on a promising new direction for NLP development, enabling semi-automated or automated development of rule-based systems with significantly faster, more cost-effective, and transparent execution compared with deep learning model-based solutions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。