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Nursing Clinical Information System (NCIS)
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用关键词优化的模板插入,通过基于提示的学习来对临床笔记进行分类.

Eugenia Alleva1,2, Isotta Landi3, Leslee J Shaw4

  • 1Windreich Department of Artificial Intelligence and Human Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA. eugeniaalessandrae.allevabonomi@mssm.edu.

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概括
此摘要是机器生成的。

关键字优化模板插入 (KOTI) 改进了基于提示的学习,用于在零和少数拍摄设置中对临床笔记进行分类,特别是在编码器模型中. 在数据有限的情况下,战略模板放置可以提高性能.

关键词:
经期不良症 经期不良症 经期不良症编码器 编码器盖托特龙 (Gatortron) 是一个提取信息 提取信息在NLP中,我们使用了NLP.快速地提醒了他们.

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科学领域:

  • 自然语言处理自然语言处理.
  • 医疗保健中的机器学习
  • 临床信息学 临床信息学

背景情况:

  • 基于提示的学习适应预先训练的语言模型 (PLM) 用于有限数据的任务.
  • 这种方法在临床环境中至关重要,因为注释数据很少.
  • 调查提示模板位置会影响临床笔记分类中的模型性能和培训效率.

研究的目的:

  • 评估提示模板位置对临床笔记分类性能的影响.
  • 引入和评估一个关键字优化模板插入 (KOTI) 方法.
  • 为了比较KOTI与标准模板插入 (STI) 在零和少数射击学习场景中.

主要方法:

  • 开发了KOTI,以便在相关的临床关键词附近放置提示模板.
  • 将KOTI与STI进行比较,使用天真的尾部截断 (STI-s) 和关键字优化截断 (STI-k).
  • 在五个临床分类任务中使用编码器模型 (GatorTron,ClinicalBERT) 和解码器模型 (BioGPT,ClinicalT5).

主要成果:

  • KOTI在零射击和少数射击学习中的编码器模型的STI-s和STI-k的表现明显优于KOTI.
  • KOTI在GatorTron的STI-k上取得了24%的F1改进,而临床BERT的F1改进为8%.
  • 解码器模型显示混合结果;KOTI改善了BioGPT (+19%F1),但降低了临床T5 (-18%F1).

结论:

  • 模板位置对于临床任务中基于提示的编码器模型微调至关重要.
  • KOTI证明了在有限的培训数据下优化临床笔记分类的潜力.
  • 在不同的变压器模型架构中,KOTI的有效性各不相同.