深度学习方法用于基于临床工作流程阶段预测的程序持续时间:一个基准研究.
Emanuele Frassini1, Teddy S Vijfvinkel1,2, Rick M Butler1
1Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands.
Computer assisted surgery (Abingdon, England)
|February 24, 2025
概括
深度学习模型,特别是基于CNN的InceptionTime,准确地预测心脏导管实验室程序结束时间. 这项技术可以形成一种自动化工具,以优化患者的调度并提高阴道实验室的效率.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 心脏导管实验室 (cath lab) 的程序需要精确的时间安排.
- 预测手术结束时间对于优化阴道实验室工作流程和患者吞吐量至关重要.
研究的目的:
- 评估深度学习模型,以预测使用视频衍生的临床阶段的心脏导管程序结束时间.
- 为此预测任务确定最准确的深度学习架构.
主要方法:
- 利用视频分析中的临床阶段作为深度学习模型的输入.
- 评估了各种模型,包括InceptionTime,LSTM-FCN,LSTM有注意力,标准LSTM和变压器.
- 使用平均绝对误差 (MAE) 和对称平均绝对百分比误差 (SMAPE) 评估预测准确性.
主要成果:
- 发明时间和LSTM-FCN显示出最高的预测准确度.
- 在60秒的间隔内,实现了MAE低于5分钟,SMAPE低于6%.
- 基于CNN的模型,特别是InceptionTime,在时间序列预测的特征提取方面表现出色.
- 变压器模型为实时应用提供了最快的推断时间.
- 一个整体模型显示出较低的错误率,但需要更长的训练.
结论:
- 深度学习模型,特别是像InceptionTime这样的CNN架构,对于准确预测阴道实验室程序结束时间非常有效.
- 这些模型可以成为自动化工具的基础,用于预测呼叫下一个患者的最佳时间,潜在的平均误差约为30秒.
- 将其集成到临床调度系统中可以提高阴道实验室的效率.
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