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使用罗神经网络进行临床自然语言处理的少数镜头学习:算法开发和验证研究

David Oniani1, Premkumar Chandrasekar1, Sonish Sivarajkumar2

  • 1Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, United States.

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PubMed
概括
此摘要是机器生成的。

罗神经网络 (SNN) 在临床自然语言处理 (NLP) 任务中表现有前途. 在低数据场景中,基于SNN的方法优于GPT-2,在临床NLP中提高了回忆和F分数.

关键词:
在FSL,FSL在FSL在NLP中,我们使用了NLP.在SNN中,SNN是SNN姆神经网络的神经网络几次射击的学习学习自然语言处理自然语言处理.神经网络的神经网络的神经网络

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科学领域:

  • 计算语言学计算语言学
  • 医疗保健中的人工智能
  • 机器学习用于临床应用.

背景情况:

  • 自然语言处理 (NLP) 对于利用电子健康记录数据至关重要.
  • 深度学习模型在临床NLP中表现出色,但需要大量的注释数据集,这些数据很少.
  • 短暂学习 (FSL) 解决了数据稀缺问题,罗神经网络 (SNN) 显示出潜力,但在临床NLP中未得到充分探索.

研究的目的:

  • 提出和评估基于罗神经网络 (SNN) 的方法,用于一些临床NLP任务.
  • 在有限的注释临床数据的情况下,研究SNNs在场景中的有效性.

主要方法:

  • 开发了两个基于SNN的FSL方法:预训练SNN和SNN与二级嵌入.
  • 在临床句子分类的评估方法使用4-shot,8-shot和16-shot设置.
  • 与变压器 (BERT),BioBERT,BioClinicalBERT和生成预训练变压器2 (GPT-2) 的双向编码器表示进行了基准测试.

主要成果:

  • 在四次射击任务中,GPT-2显示出更高的精度,但SNN (基于BioBERT的预训练) 获得了更好的回忆和F分数.
  • 基于SNN的方法在精度,回忆和F分数方面始终超过了GPT-2在8枪和16枪设置方面.
  • 在低数据的临床NLP中,SNN在基于提示的GPT-2上表现优越.

结论:

  • 拟议的SNN方法对于少数的临床NLP任务是有效的.
  • 在有限的注释临床数据的情况下,SNN为改善深度学习模型性能提供了可行的解决方案.