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使用大型语言模型自动化伤害严重性得分计算:使用大型语言模型辅助创伤评分的可行性研究.

Sheng-Yu Chan1, Pang-Chun Liao2, Albert Jow3

  • 1Department of Trauma and Emergency Surgery, Chang Gung University, Chang Gung Memorial Hospital, Taoyuan, Taiwan.

The Journal of surgical research
|December 31, 2025
PubMed
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此摘要是机器生成的。

一个大型语言模型 (LLM) 可以准确计算创伤患者的伤害严重性得分 (ISS),减少手动错误. 这种人工智能工具在创伤评分方面显示出高可靠性和准确性.

科学领域:

  • 医疗信息学医学信息学
  • 创伤外科手术是什么
  • 医疗保健中的人工智能

背景情况:

  • 伤害严重程度评分 (ISS) 对于创伤评估至关重要,但手动计算容易出现错误并耗时.
  • 目前的方法依赖于注册机构的手动评分,导致潜在的不准确和延误.

研究的目的:

  • 评估使用大型语言模型 (LLM) 进行自动化ISS计算的可行性.
  • 与手动方法相比,评估LLM辅助创伤评分的准确性和可靠性.

主要方法:

  • 在一级创伤中心的一项回顾性研究,使用了2022年患者数据.
  • 在创伤评分原则上,LLM接受了结构化提示的培训.
  • 使用100个案例进行验证,将LLM生成的ISS与注册商计算的ISS通过皮尔森相关性,ICC和布兰德-阿尔特曼分析进行比较.

主要成果:

  • 在LLM ISS和注册商ISS之间达成高度协议 (ICC=0.981).
  • 在布兰德-阿尔特曼分析中,LLM表现出高精度 (0.91) 与最小平均偏差 (-0.03).
  • 在不同的ISS范围中保持一致的性能.

结论:

关键词:
人工智能的人工智能是人工智能.伤害严重程度得分 伤害严重程度得分大型语言模型.创伤是一个创伤.

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  • 由LLM生成的ISS是一种可靠和准确的创伤评分自动化方法.
  • 有潜力简化临床工作流程,减少创伤评估中的人为错误.
  • 未来的研究应该专注于实时集成和应用到其他评分系统.