基于超声波和物理检查的机器学习模型用于气道评估
L Madrid-Vázquez1, R Casans-Francés2, M A Gómez-Ríos3
1Servicio de Anestesiología y Reanimación, Hospital Fundación Jiménez Díaz, Madrid, Spain.
Revista espanola de anestesiologia y reanimacion
|June 2, 2024
概括
机器学习模型可以使用临床和超声波数据预测困难的气道. 在"经典模型"中结合的体检变量显示出最高的预测准确性 (AUC-ROC 0.75).
科学领域:
- 麻醉学 麻醉学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 预测困难的呼吸道对于手术期间的患者安全至关重要.
- 传统方法依赖于体检,但准确性可能有限.
- 整合超声波和机器学习为改善预测提供了潜力.
研究的目的:
- 评估机器学习模型对预测困难气道的有用性.
- 为了比较临床和超声波参数的预测性能.
- 为了确定变量的最佳组合困难的气道预测.
主要方法:
- 400名接受选择性手术的患者的前性队列研究.
- 收集的临床情况 (年龄,BMI,Mallampati分数等) 和超声波的参数.
- 开发了随机森林机器学习模型,使用单个和组合的预测因素,评估AUC-ROC,灵敏度和特异性.
主要成果:
- 科尔马克-莱汉尼III级或更高的患者的年龄,BMI,宫周长和特定的体检结果显着更高.
- 使用体检变量的机器学习模型优于基于超声波的模型,尽管在统计学上并不显著.
- 结合物理变量的"经典模型"实现了最高的AUC-ROC0.75 (0.67-0.83),显著超过了超声波模型.
结论:
- 机器学习模型显示出预测困难的气道的前景.
- 结合了传统的体检变量 (
- 经典模型") 与基于超声波的模型相比,表现出优越的预测性能.
- 需要进一步开发,但机器学习为困难的气道诊断提供了可行的方法.
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