README: 通过以数据为中心的NLP来弥合医疗术语和患者教育的平民理解
Zonghai Yao1, Nandyala Siddharth Kantu1, Guanghao Wei1
1University of Massachusetts, Amherst.
概括
我们开发了一种方法,将电子健康记录 (EHR) 中的医学术语简化为患者友好的语言. 在我们的新数据集上训练的开源模型与患者教育的ChatGPT性能相匹配或超过.
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
- 患者教育 患者教育
背景情况:
- 医疗保健日益以患者为中心,强调自我护理和教育.
- 电子健康记录 (EHR) 包含复杂的医学术语,阻碍了患者的理解.
- 弥合医学知识差距对于有效的以患者为中心的护理至关重要.
研究的目的:
- 开发一个用于生成医学术语的简单定义的自动化系统.
- 简化EHR中复杂的医疗信息,以改善患者的理解.
- 通过可访问的医学语言,推进以患者为中心的医疗保健.
主要方法:
- 创建了README数据集:超过50,000个医疗术语/外界定义对与专家注释.
- 设计了一个人类-人工智能管道用于数据过,增强和选择,以提高数据质量.
- 利用检索增强生成与微调的开源模型在README数据集上训练.
主要成果:
- 在高质量的数据上微调的开源模型实现了与最先进的封闭源模型相比或更好的性能.
- 证明了README数据集和人类-AI管道在改善模型输出质量的有效性.
- 减少了幻觉,并提高了生成的简单定义的准确性.
结论:
- 自动生成的简单定义是可行的和有效的简化医疗术语.
- 高质量,精心策划的数据集使开源模型能够与先进的专有LLM竞争.
- 这项工作有助于弥补患者知识差距,并支持以患者为中心的医疗保健.
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