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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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应用大型语言模型进行外科病例长度预测.

Adhitya Ramamurthi1,2, Bhabishya Neupane3, Priya Deshpande4

  • 1Selig Hub for Surgical Data Science, Medical College of Wisconsin, Milwaukee.

JAMA surgery
|July 9, 2025
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概括
此摘要是机器生成的。

大型语言模型 (LLM) 可以准确预测外科病例持续时间,匹配或超过当前手术室安排方法. 精心调整的LLM提供了一种有前途的工具,可以使用临床笔记来提高OR效率.

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科学领域:

  • 医疗保健中的人工智能
  • 手术操作管理手术操作管理
  • 临床信息学 临床信息学

背景情况:

  • 准确预测外科病例持续时间对于高效的手术室 (OR) 管理至关重要.
  • 不有效的安排导致患者和外科医生的满意度下降,并造成重大财务损失.

研究的目的:

  • 评估大型语言模型 (LLM) 在预测外科病例长度方面的可行性和准确性.
  • 使用非结构化临床数据,将LLM的表现与现有的外科病例持续时间估计方法进行比较.

主要方法:

  • 从2017年到2023年,对125,493个选择性手术病例进行了回顾性分析.
  • 评估了包括GPT-4,GPT-3.5,Mistral,Llama-3和Phi-3在内的11个LLM,以及GPT-4和GPT-3.5.5的微调变体.
  • 预测准确性是基于平均绝对误差 (MAE) 和实际持续时间的20%内的预测百分比.

主要成果:

  • 微调的GPT-4实现了最佳性能 (MAE 47.64分钟,R2 0.61),与当前的OR调度 (MAE 49.34分钟,R2 0.63) 相比.
  • 微调的LLM (GPT-4和GPT-3.5) 在预测准确度方面显著超过当前方法 (46.12%和46.08%与40.92%,P <.001).
  • 微调的GPT-4在外部验证中表现出强的表现 (MAE 48.66分钟,精度为46.0%).

结论:

  • 精心调整的LLM可以预测外科病例的长度,其准确性与当前的机构方法相美或超过.
  • 通过改进病例长度预测,LLM显示了提高手术室效率的潜力.
  • 利用现有的临床文档与LLM提供了一个新的方法来优化外科手术安排.