术后人工智能驱动的手术综合建模,使用麻醉,身体和认知状态来预测医院结果
Sabyasachi Bandyopadhyay1, Jiaqing Zhang2, Ronald L Ison2
1Stanford University.
Research square
|December 3, 2025
概括
通过时钟绘图测试评估的手术前认知状态显著预测了手术结果,如住院和死亡率. 时钟尺寸成为不良事件的关键认知预测指标.
科学领域:
- 神经外科 神经外科
- 医疗信息学 医疗信息学
- 老年医学 老年医学
背景情况:
- 手术前的认知状态对于手术结果至关重要,但仍未得到充分研究.
- 时钟绘图测试提供了一种评估认知功能的实用方法.
- 深度学习可以从时钟图纸中提取详细的特征.
研究的目的:
- 评估手术前认知状态对手术结果的预测能力.
- 为了确定认知评估是否在预测结果方面超过了传统的风险因素.
- 确定与不良手术事件相关的特定认知和手术内特征.
主要方法:
- 使用深度学习算法进行时钟绘图测试,提取10个结构特征.
- 训练有素的机器学习模型具有5倍的交叉验证,以预测术后结果.
- 采用沙普利添加剂解释功能可解释性的解释.
主要成果:
- 术后认知数据集是18个手术结果对中的12个最好的预测因素.
- 手术持续时间和麻醉剂度是不良结果的重要预测因素.
- 低平均血压,高血压变性和时钟大小预测了不良结果.
结论:
- 手术前的认知状态,特别是通过尺寸等时钟绘图特征进行评估,是手术结果的有价值的预测指标.
- 将认知评估整合到外科手术期间的建模中,可以提高风险预测和医疗保健策略.
- 术内变量,如手术持续时间和血压管理,对于患者安全至关重要.
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