评测临床笔记分段工具，大模型表现最佳。
CNSight: Evaluation of Clinical Note Segmentation Tools
- 对比规则基线、领域模型与大语言模型的分段效果。
- GPT-5-mini在句级和自由文本分段上F1达72.4，最优。
- 适合做医疗信息抽取、病历摘要等下游任务的研究者参考。
临床笔记在从电子病历系统提取后常以非结构化或半结构化形式存储，阻碍其用于二次分析与临床应用。准确识别章节边界是结构化笔记的关键步骤，如现病史、用药记录和出院指导等章节提供不同临床背景。本文基于MIMIC-IV中的1,000份标注笔记，评估了规则基线、领域特定Transformer模型及大语言模型在临床笔记分段上的表现。实验显示，基于API的大模型整体性能最佳，GPT-5-mini在句级与自由文本分段任务中平均F1达到72.4。轻量级基线在结构化句级任务中仍具竞争力，但在非结构化自由文本上表现较差。结果为方法选择提供依据，并为信息提取、队列识别与自动摘要等下游任务奠定基础。
原文摘要 · Abstract (English)
Clinical notes are often stored in unstructured or semi-structured formats after extraction from electronic medical record (EMR) systems, which complicates their use for secondary analysis and downstream clinical applications. Reliable identification of section boundaries is a key step toward structuring these notes, as sections such as history of present illness, medications, and discharge instructions each provide distinct clinical contexts. In this work, we evaluate rule-based baselines, domain-specific transformer models, and large language models for clinical note segmentation using a curated dataset of 1,000 notes from MIMIC-IV. Our experiments show that large API-based models achieve the best overall performance, with GPT-5-mini reaching a best average F1 of 72.4 across sentence-level and freetext segmentation. Lightweight baselines remain competitive on structured sentence-level tasks but falter on unstructured freetext. Our results provide guidance for method selection and lay the groundwork for downstream tasks such as information extraction, cohort identification, and automated summarization.
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。