从临床文本中提取信息,使用生成式预训练的变压器模型.
Min-Soo Kim1,2, Philip Chung2, Nima Aghaeepour2
1Department of Anesthesiology and Pain Medicine, Anesthesia and Pain Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
International journal of medical sciences
|March 3, 2025
概括
生成式预训练变压器 (GPT) -3.5和GPT-4在临床文本提取中显示了不同的精度. 通过定义来增强提示,提高了GPT-4在复杂数据提取方面的性能.
科学领域:
- 在医疗保健中的自然语言处理.
- 临床研究中的人工智能
- 生物医学信息学 生物医学信息学
背景情况:
- 临床文本分析由于其非结构化的性质而复杂.
- 像GPT这样的大型语言模型 (LLM) 提供了信息提取的潜力.
- 对比不同的GPT版本对于优化临床数据处理至关重要.
研究的目的:
- 为了比较GPT-3.5和GPT-4在临床文本上的信息提取性能.
- 评估快速工程对临床数据的LLM准确性的影响.
- 确定在临床文本分析中使用LLM的最佳策略.
主要方法:
- 从病例报告中利用了三种类型的临床文本 (患者特征,病史,测试结果).
- 应用简单的提示和贪的方法解码策略来提取信息.
- 实施了替代解码策略和特定任务的定义,以实现快速增强.
主要成果:
- 两种GPT模型都通过简单的提示准确地提取了简单的信息.
- 在性别提取方面,GPT-4比GPT-3.5 (70%) 获得了更高的精度 (95%).
- 在BMI提取方面,GPT-3.5的表现优于GPT-4的表现 (78%与57%相比);迅速的提炼提高了GPT-4的准确性.
结论:
- GPT模型适合于简单的临床文本提取任务.
- 提示中的特定任务定义比复杂提取的简单提示更有效.
- 专家快速设计和结果监测对于在临床环境中可靠使用LLM至关重要.
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