MedReadMe:医学领域细粒度句子可读性的系统研究
Chao Jiang1, Wei Xu1
1College of Computing, Georgia Institute of Technology.
概括
研究人员开发了MedReadMe,这是一个用于测量医疗文本可读性的新数据集. 将术语跨度计数添加到可读性公式中,可以显著提高复杂医学语言的准确性.
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.
- 卫生沟通健康沟通
背景情况:
- 医学文本为患者和医疗保健专业人员带来了重大可读性挑战.
- 准确测量医疗文本可读性对于改善可访问性和理解性至关重要.
研究的目的:
- 系统地研究医学领域的细粒度可读性测量在句子和跨度层面.
- 引入和验证一个新的数据集,MedReadMe,用于医学可读性研究.
- 为了对医疗文本的现有可读性指标进行比较和改进.
主要方法:
- 开发了MedReadMe数据集,其中包含4,520个手动注释的句子,以提高可读性和复杂的跨度.
- 通过650个语言特征和自动术语识别进行了定量分析.
- 基于非监督,监督和大型语言模型 (LLM) 的可读性指标.
主要成果:
- 改善了医疗领域的最先进的句子级可读性指标.
- 证明将术语跨度计数纳入其中显著提高了与人类判断的相关性.
- 确定了细粒度医学文本可读性评估的关键特征.
结论:
- MedReadMe数据集和改进的指标为评估医疗文本可读性提供了增强的工具.
- 针对复杂的跨度,特别是术语,对于准确的可读性评估至关重要.
- 未来的工作可以利用这些发现来开发更容易获得的医疗沟通工具.
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