从临床叙述中提取规范化症状的提取,使用大型语言模型
1University of Chicago, Chicago, IL, United States of America.
概括
大型语言模型 (LLM) 从临床文本中有效地提取患者的症状概念. 这种方法超越了传统方法,改善了医疗数据分析和研究潜力.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 医疗保健中的人工智能
背景情况:
- 患者的症状对于临床决策至关重要,但在电子健康记录中通常是无结构的.
- 从自由临床文本中提取症状信息存在重大挑战.
- 目前用于提取症状的方法在范围和准确性方面有限.
研究的目的:
- 评估大型语言模型 (LLM) 在从临床叙述中提取症状概念的有效性.
- 将LLM的表现与传统的特定症状的机器学习分类器进行比较.
- 探索LLMs在改善医疗保健工作流程和研究方面的潜力.
主要方法:
- 使用大型语言模型 (LLM) 及时工程技术,包括澄清信息,短暂的示例和思维链提示.
- 将LLM性能与在映射临床概念上训练的症状特异机器学习分类器进行比较.
- 用F1评分来评估常见症状概念的表现.
主要成果:
- 与机器学习分类器相比,LLM在提取大多数症状概念方面表现优异.
- 在LLM方法取得更高的F1得分,表明提高了准确性和有效性.
- 在临床叙述中,LLM可能会利用上下文信息来更好地使症状正常化.
结论:
- 大型语言模型为从非结构化的临床文本中解锁有价值的症状信息提供了一个有希望的方法.
- 基于LLM的症状提取可以增强临床工作流程,并支持各种研究倡议.
- 这项技术有可能在医疗保健中显著推进临床叙事数据的使用.
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