纵向机器学习模型用于预测心力衰竭患者的缩血压
Roya Najafi-Vosough1, Javad Faradmal1,2, Seyed Kianoosh Hosseini3
1Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
机器学习,特别是MLS-SVR,有效地预测心力衰竭 (HF) 患者的缩血压 (SBP),优于传统模型. 关键预测因素包括高血压史,BMI,和甘油三.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 系统性血压 (SBP) 是心力衰竭 (HF) 患者的关键预后指标,与死亡率和再接收风险直接相关.
- 有效的血压管理对于优化高血压患者的治疗结果至关重要.
研究的目的:
- 在HF患者中随着时间的推移确定与SBP波动相关的显著变量.
- 为了比较经典 (线性混合效应模型 - LMM) 和机器学习 (混合效应最小平方支向量回归 - MLS-SVR) 模型对SBP的预测性能.
主要方法:
- 一项回顾性队列研究分析了2015年10月至2019年7月期间住院的483名HF患者的数据.
- 使用LMM和MLS-SVR进行SBP预测,模型有效性通过平均绝对误差和根平均平方误差进行评估.
主要成果:
- LMM确定了性别,BMI,,时间和高血压史作为影响SBP变化的重要因素 (P <0.05).
- MLS-SVR确定了高血压史,,BMI和甘油三水平作为SBP前四大预测因素.
- 在训练和测试数据集中,MLS-SVR在LMM上表现优越.
结论:
- 该MLS-SVR模型显示承诺作为一个强大的替代传统的纵向模型预测SBP在HF患者.
- 使用先进的机器学习技术准确的SBP预测可以帮助个性化HF管理策略.
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