液晶显示器基准:用于语言模型的死亡率预测的长期临床文档基准.
WonJin Yoon1,2, Shan Chen1,2,3,4, Yanjun Gao5
1Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States.
Journal of the American Medical Informatics Association : JAMIA
|November 27, 2024
概括
为了预测患者死亡率,开发了一套用于长期临床文档分类的新基准数据集. 这一数据集挑战了当前的模型,但它们可以在漫长的临床笔记中识别有意义的预测信号.
科学领域:
- 临床信息学 临床信息学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 机器学习
背景情况:
- 临床文档包含丰富的非结构化数据,对医疗保健洞察至关重要.
- 现有的自然语言处理 (NLP) 基准数据集对于长长的临床文档是不够的.
- 缺乏基准标准阻碍了临床文本分析模型的开发和评估.
研究的目的:
- 引入长篇临床文档 (LCD) 基准标准,用于分类长篇临床文本.
- 通过使用出院笔记,能够预测30天的医院外死亡率.
- 促进NLP模型的开发和评估,以了解临床文档.
主要方法:
- 开发了使用MIMIC-IV出院说明和全州死亡数据的LCD基准.
- 评估了各种模型的基准,包括词袋,CNN和大型语言模型.
- 对模型输出进行了全面的分析,包括手动审查和重量可视化.
主要成果:
- 液晶显示器基准显示器具有临床笔记,平均词数为1687.
- 最好的监督模型获得了28.9%的F1评分,而GPT-4获得了32.2%的F1评分.
- 模型性能表明数据集具有挑战性,但包含有价值的预测信号.
结论:
- 液晶显示器基准对当前的NLP模型和人类专家来说是一个重大挑战.
- 开发的模型证明了从长长的临床文件中提取有意义的预测信息的能力.
- 预计LCD基准将推动临床NLP监督和提示方法的进步.
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