序列标签用于消除医学缩写的模糊性
Mucahit Cevik1, Sanaz Mohammad Jafari1, Mitchell Myers1
1Toronto Metropolitan University, 44 Gerrard St E, Toronto, M5B 1G3 Ontario Canada.
Journal of healthcare informatics research
|November 6, 2023
概括
序列标记方法有效地消除了医学缩写的模糊性,超过了传统的文本分类. SciBERT表现出强的表现,突出了其用于理解复杂的临床笔记的实用性.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 医疗文本经常使用缩写来提高效率和保护患者的隐私.
- 缩写的模糊性 (多种含义) 在临床文档中构成了重大挑战.
- 缺乏标准化的映射系统使医疗缩写的明确性变得复杂.
研究的目的:
- 评估序列标记技术的有效性,用于医学缩写的明确化.
- 评估序列标记能够在一个文本中处理多个独特的缩写的能力.
- 为了比较预先在科学和医疗机构中训练过的变压器模型来完成这个任务.
主要方法:
- 利用两个公共数据集来评估模型性能.
- 与几种变压器模型进行比较,包括那些在专业公司上预先训练的变压器模型.
- 与文本分类模型相比,实施和评估了序列标记方法.
主要成果:
- 提出的序列标签方法超过了标准文本分类模型的性能.
- 在序列标记和文本分类方面,SciBERT模型表现出强大的性能.
- 文本分类模型仅在应用后处理过器后才能达到与序列标签相似的性能.
结论:
- 序列标记是一种可行和有效的方法,用于医学缩写的明确化.
- 变压器模型,特别是SciBERT,在提高自动化临床笔记分析的准确性方面显示出显著的前景.
- 后处理对于提高文本分类模型在缩写清晰度方面的性能至关重要.
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