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在代表性不足的语言中加速临床文本注释:关于文本非识别的案例研究.

He A Xu1, Valentin Loftsson1, Bogdan Kulynych1

  • 1Biomedical Data Science Center, Lausanne University Hospital (CHUV) and Lausanne University, Lausanne, Switzerland.

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概括

通过竞赛和预注释来激励注释者,可以显著加快临床笔记的命名实体识别 (NER). 这种方法提高了注释质量,并使高性能文本去识别模型成为可能.

关键词:
临床注意事项 临床注意事项取消身份的识别 取消身份的识别在NLP中,我们使用了NLP.名称 实体识别 名称 实体识别

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科学领域:

  • 自然语言处理自然语言处理.
  • 临床信息学 临床信息学
  • 机器学习 机器学习

背景情况:

  • 临床笔记是研究和质量监测的丰富数据来源.
  • 命名实体识别 (NER) 构建了非结构化的临床文本,但由于有限的专业培训数据,特别是在医院环境中,它面临着挑战.
  • 标注成本和当地特点进一步复杂化了NER模型的开发.

研究的目的:

  • 调查加速临床NER注释过程的策略.
  • 评估游戏化 (主动竞赛) 和预注释对注释效率和质量的影响.
  • 为法国临床笔记开发一个高性能文本非识别模型.

主要方法:

  • 实施了一项主动竞赛,以激励人类注释者进行文本非识别任务.
  • 为参与者提供预注释,以评估它们对注释性能 (回忆和精度) 的影响.
  • 将这些策略应用于瑞士大学医院的法国临床笔记和出院摘要.

主要成果:

  • 积极的竞赛和平均质量的预注释显著减少了注释时间.
  • 这些综合策略显然提高了注释质量.
  • 法国临床笔记的文本非识别模型获得了高F1得分,为0.94.

结论:

  • 将主动竞赛与预注释相结合是加速临床NER的有效方法.
  • 这些策略提高了注释过程的速度和质量.
  • 开发的方法可以为敏感的临床数据创建可靠的非识别模型.