在临床叙述中缩写词的词语意义的明确化
Daphné Chopard1, Padraig Corcoran1, Irena Spasić1
1School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom.
Frontiers in digital health
|March 14, 2024
概括
本研究通过使用科学摘要来创建培训数据来解决临床文本中的模两可的缩写. 一个神经网络实现了95%的准确度在明确缩写词,改善临床叙事的解释.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 临床叙述经常使用没有定义的缩写,导致模糊性和自动解释的挑战.
- 监督学习受限于患者隐私问题和手动数据注释的高成本.
研究的目的:
- 开发一种自动创建大型注释数据集的方法,用于训练缩写解模型.
- 提高从临床文本中提取缩写词含义的准确性和效率.
主要方法:
- 利用科学摘要生成一个大型,自动注释的数据集模拟的缩写.
- 在这个数据集上训练了一个神经网络.
- 集成训练有素的网络与多词术语识别,以便在临床叙述中快速提取缩写的意义.
主要成果:
- 神经网络在科学摘要中获得了95%的F1分数,用于缩写词的明确性.
- 从放射学报告中提取缩写的感觉,获得了F1得分的74%.
结论:
- 科学摘要可以有效地克服临床研究中的数据限制.
- 提出的方法有助于制定机构特定的缩写库,以改善临床文本分析.
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