在紧急医疗中使用大型语言模型生成合成医疗保健对话
Denis Moser1, Matthias Bender1, Murat Sariyar1
1Bern University of Applied Sciences, Switzerland.
Studies in health technology and informatics
|November 22, 2024
概括
我们使用大型语言模型 (LLM) 开发了一个管道,用于生成用于训练自然语言处理 (NLP) 系统的合成医疗对话. 该LLM管道实现了高准确性,证明了创建有价值的医疗保健数据集的潜力.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 为自然语言处理 (NLP) 获取标记的医疗数据集是具有挑战性的,因为隐私问题.
- 大型语言模型 (LLM) 提供了从非结构化临床文本中提取结构化数据的潜力.
- 需要采用黄金标准数据集来评估在医疗保健环境中的NLP模型.
研究的目的:
- 开发和评估基于LLM的管道,用于生成合成医学对话.
- 从救护车工作人员与患者的对话中提取信息,以填写紧急协议表格.
- 为培训和评估紧急医疗中的NLP系统创建一个黄金标准数据集.
主要方法:
- 一个管道设计使用"Zephyr-7b-beta"用于初始对话生成和"GPT-4 Turbo"用于改进和翻译.
- 相关的医学数据使用了MIMIC-IV数据库.
- 评估涉及使用Retrieval-Augmented Generation (RAG) 的准确性评估和使用多语言模型的情绪分析.
主要成果:
- 与"Zephyr-7b-beta"的初始对话生成实现了94%的准确性.
- 在使用"GPT-4 Turbo"进行精炼后,准确度略有下降至87%.
- 情绪分析显示,在改进后,情绪向更积极的情绪转移.
结论:
- 在NLP开发中,LLM显示出产生合成医学对话的巨大潜力.
- 在优化LLM性能和确保医疗保健应用数据质量方面仍然存在挑战.
- 这种方法可以为未来用于紧急医疗服务的NLP系统的开发提供信息.
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