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一种预先训练的语言模型方法,用于为手术前麻醉诊所的手术患者进行分组

Nicole Y Xu¹, Onkar Litake², Jeffrey L Tully²

¹School of Medicine, University of California, San Diego, La Jolla, CA, 92037, USA.

Journal of clinical monitoring and computing

|December 24, 2025

概括

预先训练有素的语言模型可以有效地对手术患者进行麻醉评估. 与诊断代码和临床文本相结合的RoBERTa实现了最高的准确性,减少了提供者的负担.

科学领域:

人工智能在医学中的应用
自然语言处理自然语言处理.
医疗保健信息学医疗保健信息学

背景情况:

术前麻醉评估对于患者安全和术后护理至关重要.
目前的评估方法由于患者异质性而面临效率低下的问题.
在手术前麻醉评估中,优化资源配置至关重要.

研究的目的:

评估预先训练的语言模型在选手术患者的有效性,以进行适当的手术前麻醉评估.
为此任务比较不同语言模型和数据输入组合的性能.

主要方法:

从一个单一的手术前麻醉护理诊所回顾分析了1761名手术患者的数据.
评估了四种预先训练的语言模型 (RoBERTa,BERT,临床BERT,PubMedBERT).
模型使用诊断代码 (D),临床文本 (N) 和组合 (D+N) 数据输入进行评估,性能指标包括F1得分和AUC.

主要成果:

综合数据输入 (D+N) 给出了跨模型的最高曲线下面积 (AUC) 范围 (0.70-0.74).
与其他模型相比,RoBERTa在所有数据输入类型中表现出优异的性能.
临床文本 (N) 分析显示中等性能 (AUC 0.58-0.73),而诊断代码 (D) 单独是最低的 (AUC 0.57-0.62).

结论:

使用预先训练有素的语言模型进行自动分组可以显著提高手术前麻醉评估的准确性和效率.
整合临床文本和诊断代码可以提高患者分组的模型性能.
这种方法有可能减少手动审查,并减轻在手术前护理环境中提供者的工作负担.

关键词:

人工智能的人工智能是人工智能.语言模型语言模型自然语言处理自然语言处理.手术前护理诊所手术前护理诊所术前医学术前医学

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