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机器学习模型用于预测ACS后的严重不良住院事件

Hui Gao^1,2,3, Xuanze Liu², Dongyuan Sun²

¹Graduate School of Dalian Medical University, Dalian Medical University, No. 24, Luxun Road, Zhongshan District, Dalian City, Liaoning Province 116044, China.

Postgraduate medical journal

|March 4, 2025

概括

这项研究开发了风险模型,以预测急性冠状动脉综合征 (ACS) 患者的严重不良住院事件. 机器学习和物流回归模型确定了关键预测因素,帮助临床决策和改善患者的治疗结果.

科学领域:

心脏病学心脏病学
医疗信息学医疗信息学
医疗保健中的机器学习

背景情况:

已知急性冠状动脉综合征 (ACS) 患者在医院死亡的风险因素,但来自中国大型队列的现实数据需要进一步探索.
在ACS患者中预测严重不良住院事件 (SAHE) 对于及时干预和改善预后至关重要.

研究的目的:

开发和验证风险分层模型,以预测ACS患者的SAHE (死亡率,心脏休克,心脏骤停).
将机器学习模型的性能与SAHE预测的后勤回归进行比较.
为临床医生提供经过验证的在线工具,用于对ACS患者的风险评估.

主要方法:

一项使用CCC-ACS项目数据库的队列研究.
使用五种机器学习算法 (XGBoost,RF,MLP,KNN,堆叠) 和后勤回归的临床预测模型的开发.
使用诸如曲线下的面积 (AUC),准确性和决策曲线分析等指标评估模型性能.

主要成果:

年龄,糖尿病,功能衰竭,心率,冲击指数,KILLIP得分和入院时的心脏骤停被确定为SAHE的独立预测因素.
随机森林 (RF) 和XGBoost模型显示出强烈的歧视 (AUC> 0.81),与后勤回归 (AUC> 0.81) 相似.
基于机器学习和物流回归模型开发了两个在线风险计算器.

结论:

机器学习方法对于在ACS患者中识别SAHE预测因子是可行的和实用的.
开发的CCC-ACS风险评分提供了经过验证的工具,以帮助临床决策和改善ACS患者的预后.
早期识别高风险的ACS患者可以显著降低住院死亡率,并改善整体患者的治疗结果.

关键词:

急性冠状动脉综合征急性冠状动脉综合征机器学习是机器学习.风险预测模型的风险预测模型严重的不良住院事件

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