液晶显示器基准:语言模型对死亡率预测的长期临床文档基准.
medRxiv : the preprint server for health sciences
|April 8, 2024
概括
一个新的基准数据集,LCD基准,解决了长期临床文档分类资源的缺乏. 它有助于开发模型,从冗长的临床笔记中预测患者死亡率.
科学领域:
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 临床文档包含有价值的非结构化数据,对医疗保健洞察至关重要.
- 自然语言处理 (NLP) 方法对于从临床文本中提取信息至关重要.
- 现有的基准数据集不足以评估长篇临床文档上的NLP模型.
研究的目的:
- 介绍LCD基准,这是一个用于长期临床文档分类的新型数据集.
- 通过使用出院笔记,方便预测30天医院外死亡率.
- 为开发和评估临床领域的NLP模型提供标准化资源.
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 (LLM).
- 进行了对模型输出的全面分析,包括手动审查和重量可视化.
主要成果:
- 该数据集包含临床笔记,平均词数为1687个.
- 性能最好的监督模型获得了28.9%的F1得分,而GPT-4达到32.2%.
- 基准对模型和人类专家来说都是一个挑战,但模型证明了识别相关预测信号的能力.
结论:
- 液晶显示器基准为在临床文本分析中推进NLP提供了宝贵的资源.
- 它支持开发复杂的监督模型和提示技术,用于长时间的临床文档.
- 该数据集是公开可用的,以促进临床NLP的研究.
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