一个机器学习堆叠模型准确地估计了在接受选择性镇静性胃肠内镜检查的患者的胃液量
Yuqing Yan1,2, Yuzhan Jin1,2, Yaoyi Guo3
1School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
Postgraduate medicine
|March 22, 2024
概括
一个新的机器学习模型使用护理点超声波 (POCUS) 测量准确估计胃液体体积. 这种堆叠模型,集成到网页计算器中,改进了在接受胃肠内镜术的患者的传统方法.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 麻醉学 麻醉学
背景情况:
- 传统的护理点超声波 (POCUS) 用于胃液体体积的评估具有中等的准确性.
- 准确的胃液体体积估计对于接受镇静性胃肠内镜 (GIE) 的患者至关重要.
研究的目的:
- 开发和验证先进的机器学习 (ML) 模型,以更准确地估计胃液体体积.
- 为麻醉科医生创建一个实用的临床工具.
主要方法:
- 对1386名接受选择性镇静性GIE的患者进行了回顾性分析.
- 开发和评估六个ML模型和使用POCUS数据的堆叠模型 (D1:尾直径,D2:前后直径).
- 使用SHapley添加式扩展 (SHAP) 评估的重要变量.
主要成果:
- 一个堆叠模型 (线性回归+多层感知子) 实现了最高调整的R平方值为0.718.
- 该模型显示平均绝对误差 (MAE) 为4.008毫升,表现优于线性模型.
- 胃液体体积的预测在右侧位 (RLD) 测量时更准确.
结论:
- 开发的堆叠模型及其相关的网络计算器为估计胃液量提供了一种实用而准确的方法.
- 麻醉师建议在RLD位置测量胃超声波参数 (D1和D2),以提高准确度.
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