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.
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.
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.
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