开发和比较基于机器学习的模型,用于在急性心肌梗塞后预测心力衰竭
Xuewen Li1, Chengming Shang2, Changyan Xu3
1Department of Laboratory Medicine, First Hospital of Jilin University, Changchun, China.
BMC medical informatics and decision making
|August 24, 2023
概括
这项研究开发了使用XgBoost算法预测急性心肌梗塞 (AMI) 后心力衰竭 (HF) 的HF-Lab9模型. 该模型实现了高精度,帮助临床医生在早期HF风险评估.
科学领域:
- 心脏病学 心脏病学
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
背景情况:
- 心力衰竭 (HF) 是急性心肌梗塞 (AMI) 之后的一个显著并发症.
- 现有机器学习 (ML) 模型对HF后AMI的预测准确性需要进一步评估.
- 在AMI患者中确定可靠的HF预测因子对于及时干预至关重要.
研究的目的:
- 为了比较7个ML算法,用于预测AMI患者的HF.
- 开发一种最佳的预测模型,用于AMI后HF发生.
- 建立HF早期预警的外部验证模型.
主要方法:
- 利用了两组AMI患者 (2018-2019年和2020-2021年) 的例行测试数据.
- 评估了七个ML算法,包括特征选择.
- 采用ROC和DCA曲线来评估诊断疗效和临床效用.
主要成果:
- 在七个ML模型中,XgBoost算法展示了卓越的性能.
- 通过XgBoost识别的关键预测因素包括热素I,甘油三和尿液分析参数.
- 使用XgBoost开发的HF-Lab9模型实现了0.966的AUC,具有显著的临床益处.
结论:
- XgBoost被确定为AMI患者中HF预测的最佳ML算法.
- 该HF-Lab9模型提高了临床准确性在评估HF风险后AMI.
- 这项研究为未来的心血管研究中的ML模型开发提供了宝贵的参考.
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