用检索增强生成技术自动提取护士记录中的临床信息。
Retrieval-Augmented Generation Based Nurse Observation Extraction
- 基于RAG的自动化提取方法，结合外部知识增强生成。
- 在MEDIQA-SYNUR数据集上F1值达0.796，性能稳定。
- 适合医疗信息化、智能护理系统开发者参考。
大型语言模型（LLMs）的最新进展在多个领域显著降低了人工负担，这一趋势正逐步延伸至医疗领域。本文提出一种自动化流程，旨在通过自动从护士口述记录中提取临床观察，减轻护士工作压力。为确保提取准确性，我们引入基于检索增强生成（RAG）的方法。实验结果表明，该方法在MEDIQA-SYNUR测试数据集上取得了0.796的F1分数，表现优异。
原文摘要 · Abstract (English)
Recent advancements in Large Language Models (LLMs) have played a significant role in reducing human workload across various domains, a trend that is increasingly extending into the medical field. In this paper, we propose an automated pipeline designed to alleviate the burden on nurses by automatically extracting clinical observations from nurse dictations. To ensure accurate extraction, we introduce a method based on Retrieval-Augmented Generation (RAG). Our approach demonstrates effective performance, achieving an F1-score of 0.796 on the MEDIQA-SYNUR test dataset.
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