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通过文本分类方法识别和分析未计划的重新操作的原因

Zhancheng Liang¹, Wenyang Huang¹, Hongyu Xu¹

¹Shensi Lab, Shenzhen Institute for Advanced Study, UESTC, Shenzhen, China.

Scientific reports

|November 10, 2025

概括

计划外的重新操作 (URs) 带来风险并增加成本. 本研究介绍了UR-Net,这是一种使用深度学习的自动化框架,用于从临床笔记中识别UR及其原因,从而改善患者护理.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
临床数据分析临床数据分析

背景情况:

无计划的重新手术 (URs) 显著增加了患者的死亡率,住院时间和医疗保健费用.
从广泛的临床文档中手动识别UR是劳动密集型的,容易产生偏见,缺乏效率.
现有的研究缺乏使用深度学习和自然语言处理的UR的自动分类方法.

研究的目的:

开发和评估一个自动化的框架,UR-Net,用于识别计划外的重新操作 (UR),并从病房周围的文档中对其原因进行分类.
利用深度学习和自然语言处理技术进行高效和公正的UR分析.
提供一个可扩展的解决方案,用于质量控制和减少医疗保健机构的UR.

主要方法:

UR-Net框架集成了UR识别 (URNet-XL) 和原因分类 (URNet-GT).
URNet-XL采用基于XLNet模型的批量融合方法来处理长长的临床文本.
URNet-GT使用了预训练模型,具有多头注意力和双向Gated Recurrent Unit,用于多类原因分类,并结合了几次射击学习.

主要成果:

UR-Net框架实现了高加权的F1分数,即96.34%的UR识别和93.37%的原因分类,优于基线方法.
接收器运行特征曲线下的面积 (AUC) 达到97.86%,在不平衡的数据集上表现出色的分类性能.
拟议的模型显示了对计划外重新操作的准确和自动化分析的巨大潜力.

结论:

UR-Net框架提供了一种新的,自动化的方法,用于从非结构化的临床文本中识别非计划的重新手术及其潜在原因.
这种基于深度学习的方法与传统的手动方法相比,提高了UR分析的效率和准确性.
成功实施UR-Net为减少非计划的重复手术发生率和改善患者结果提供了途径.

关键词:

医疗质量控制医疗质量控制医学文本医学文本文字分类文本分类文本分类不规划的重新启动.

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