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用大型语言模型提高创伤选准确性:与人类专家决策的比较

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 创伤外科 手术 创伤外科

背景情况:

  • 准确的医院前创伤分类对于患者的治疗结果和医疗保健系统的效率至关重要.
  • 大型语言模型 (LLM) 为增强医院前创伤分拣过程提供了一个新的机会.
  • 目前在医院前创伤护理中LLM的实施是有限的.

研究的目的:

  • 评估LLM在儿科创伤分类中的表现.
  • 为了评估LLM辅助的医院前电信的准确性.
  • 为了比较LLM选准确度与人类临床医生的表现.

主要方法:

  • 在一级中心对133例儿科创伤激活的回顾性队列研究.
  • 对EMS录音,创伤页面和伤害严重程度得分 (ISS) 的分析.
  • 利用OpenAI Whisper进行转录和命名实体识别 (NER) 进行结构化数据提取;前性评估涉及创伤外科医生.

主要成果:

  • 在回顾性分析中,LLM分组显示了与人类临床医生相比的准确性 (83.5%与78.9%).
  • 通过LLM辅助的分拣显示了减少不足分拣 (4.8%对5.1%) 的趋势,并显著减少过多分拣 (58.6%对71.8%).
  • 展望评估表明,在LLM暴露后,人类选准确度有所提高,提高了正确选决策的几率.

结论:

  • 在回顾性儿科创伤分类中,LLM的准确性与创伤工作人员的准确性相当.
  • 使用结构化的"基本转录"显著减少了数据长度,同时保持了准确性.
  • 进一步的研究对于验证LLM通用性,临床结果和用户接受广泛部署至关重要.