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选择性分类与机器学习不确定性估计提高了ACS预测:在医院前环境中的回顾性研究

Juan Jose Garcia¹, Rebecca Kitzmiller², Ashok Krishnamurthy³

¹University of North Carolina at Chapel Hill, Department of Computer Science, Chapel Hill, 27514, United States.

Research square

|June 17, 2024

概括

结合渐变增强决策树和选择性分类的新机器学习方法显著提高了在医院前环境中识别急性冠状动脉综合征 (ACS) 的准确性,从而提高了患者的护理.

科学领域:

心脏病学和紧急医疗医学
医疗保健中的人工智能
机器学习用于临床决策支持

背景情况:

在医院前环境中及时识别急性冠状动脉综合征 (ACS) 对于尽量减少心肌损伤至关重要.
现有的机器学习模型显示不够准确,无法可靠地在医院内或外的ACS中进行裁决.
目前的医院前 ACS 诊断工具存在性能差距.

研究的目的:

确定一种改进的机器学习方法,用于准确的医院前ACS分类.
评估集合梯度增强决策树 (GBDT) 和选择性分类 (SC) 对于ACS诊断的有效性.
为了弥合当前医院前ACS诊断能力的绩效差距.

主要方法:

在医院前患者数据上对GBDT和SC方法的回顾性评估.
分析包括连续患者胸痛通过救护车运输到急诊室.
在ACS分类任务中使用了23个医院前共变量.

主要成果:

与现有方法相比,结合的GBDT+SC模型表现出优越的性能.
在ACS分类中,GBDT+SC提高了8%的灵敏度和23%的特异性.
合并模型提供了更高的安全性,用于在医院前和医院外的ACS.

结论:

GBDT+SC 融合代表了医院前 ACS 识别的重大进步.
这种新的方法为紧急医疗服务提供了更安全,更准确的工具.
提高诊断准确度可以导致更及时,更有效的ACS治疗.

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