使用人工智能 (自然语言处理) 来进行抽象查,提高手术中的系统审查效率
Gordon C Wong1, Robert L Kane2, Cheng-C J Chu3
1Section of Plastic Surgery, Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA.
The Journal of hand surgery, European volume
|November 15, 2024
概括
这项研究训练了一种自然语言处理 (NLP) 模型,以改善手术系统审查查. 在NLP模型加速抽象选31%而不会降低准确度.
科学领域:
- 整形外科手术 整形外科手术
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 系统性审查对于以证据为基础的医学至关重要,但耗时.
- 抽象选是系统审查中的一个主要瓶.
- 自动化选可以提高效率并减少审核员的负担.
研究的目的:
- 开发和评估一种自然语言处理 (NLP) 模型,用于选手术研究摘要.
- 评估在系统审查选中使用NLP的效率提升和准确性.
- 识别与手术干预和结果相关的摘要中的关键文本元素.
主要方法:
- 有注释的1600手术摘要用于NLP模型培训.
- 训练了一种NLP模型,以识别研究摘要中的关键文本元素.
- 进行了两项实验,处理了200个摘要,在纳入/排除决策时使用NLP辅助.
主要成果:
- 在识别关键文本元素,特别是研究干预方面,NLP模型实现了0.91的准确性.
- 使用NLP模型,平均抽象查时间减少了31%.
- 模型精度在第二次实验中得到了改进,这表明了取决于上下文的性能.
结论:
- 在手术中,NLP模型可以显著简化手术系统审查的抽象查.
- 这种方法通过准确识别原始研究和相关文本来提高效率.
- 在骨科研究中,NLP工具为加速证据合成提供了一个有希望的解决方案.
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