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相关概念视频

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提高法国临床笔记分类的变压器性能,使用有限数据集上的专家混合.

Thanh-Dung Le1,2, Philippe Jouvet3, Rita Noumeir1

  • 1Biomedical Information Processing Laboratory, École de Technologie SupérieureUniversity of Quebec Quebec City QC G1K 9H6 Canada.

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概括

一个新的专家混合 (MoE) 变压器模型有效地分类小的法国临床文本. 这种NLP方法为数据和计算资源有限的医院提供了更快,更可行的替代方案.

关键词:
贝尔特 (BERT) 公司临床自然语言处理 临床自然语言处理混合的专家组合.变压器 变压器 变压器心脏衰竭是因为心脏衰竭.

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科学领域:

  • 自然语言处理 (NLP) 是一种自然语言处理.
  • 机器学习 机器学习
  • 临床信息学 临床信息学

背景情况:

  • 变压器模型在NLP中表现出色,但由于计算需求,在小规模的临床文本分类方面遇到了困难.
  • 现有的生物医学预训练模型 (例如CamemBERT-bio,DrBERT) 是计算密集型的,限制了它们在医院环境中的使用.
  • 需要在资源有限的医院环境中为法国临床叙述提供高效的NLP解决方案.

研究的目的:

  • 开发和评估一个定制的专家混合 (MoE) 变压器模型,用于分类小规模的法国临床文本.
  • 为解决内部医院应用有限数据和低资源计算的挑战.
  • 在资源有限的环境中为临床文本分析提供实用的NLP替代方案.

主要方法:

  • 开发一个定制的MoE变压器架构.
  • 培训和评估来自CHU Sainte-Justine医院的法国小型临床文本.
  • 对DistillBERT,CamemBERT,FlauBERT和标准变压器模型进行比较分析.

主要成果:

  • 能源部变压器实现了87%的准确度,87%的精度,85%的回忆率和86%的F1分数.
  • 在同一数据集上表现优于DistillBERT,CamemBERT,FlauBERT和变压器模型.
  • 实现的训练速度至少比可比的生物医学BERT模型快190倍,尽管性能略低.

结论:

  • 定制的MoE-Transformer提供了一个计算效率高,有效的解决方案,用于分类小的法语临床文本.
  • 在有限的临床环境中,为高资源的生物医学BERT模型提供了可行的替代方案.
  • 显示了将其纳入临床决策支持系统的巨大潜力,特别是在儿科重症监护病房.