使用自然语言处理自动选摘要用于神经外科创建指南
Vijay Nitturi1, Alex Flores1,2, David F Bauer1,2
1Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA.
Neurosurgery
|April 14, 2025
概括
像Gemini Pro和ChatGPT这样的大型语言模型 (LLM) 可以自动进行神经外科抽象查,大大减少了从几周到几个小时的审查时间. 双子Pro在识别指南创建的相关摘要方面表现出更高的灵敏度 (95%).
科学领域:
- 神经外科 神经外科
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 神经外科文献的指数增长需要有效的系统审查方法和指导方针开发.
- 手动抽象选是耗时和劳动密集的.
- 大型语言模型 (LLM) 为自动化这一过程提供了一个有希望的解决方案.
研究的目的:
- 评估Gemini Pro和ChatGPT-4o-mini在自动化神经外科指南创建抽象查方面的有效性.
- 将这些LLM的性能与手动选择标准进行比较.
主要方法:
- 通过使用Gemini Pro和ChatGPT-4o-mini开发了新的管道.
- 从EMBASE搜索特定的神经外科指南 (Chiari I形) 的摘要进行了选.
- 使用了一个简化的研究问题,包含两个包含/排除标准.
主要成果:
- 双子Pro实现了95%的灵敏度和60%的特异性,正确排除了1764个摘要中的1043.
- 聊天GPT-4o-mini实现了18%的灵敏度和95%的特异性,正确排除了1066个摘要.
- 两条LLM管道在不到一个小时内完成了选.
结论:
- 通过LLM管道,可以自动对神经外科指导方针进行抽象选,大大减少处理时间.
- 这项技术有可能为跨医学领域的基于证据的指导方针提供实时更新.
- 需要进一步验证,但这种方法对高效的文献审查有希望.
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