在家用血压监测中使用机器学习预测测量连续性
Asami Matsumoto1,2, Yohei Mineharu1,3, Hirohiko Kohjitani1,3
1Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, Kyoto, Japan.
概括
机器学习可以预测高血压患者何时停止在家监测血压. 这使得早期干预可以改善粘附和血压控制.
科学领域:
- 心血管医学 心血管医学
- 医疗信息学 医疗信息学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 高血压是心血管疾病的主要危险因素.
- 一贯的家庭血压监测至关重要,但难以遵守.
- 预测和干预测量不活动是需要更好的高血压管理.
研究的目的:
- 开发一种机器学习模型,预测健康应用程序用户的测量不活动.
- 为了确定与血压监测的遵守程度下降相关的关键风险因素.
- 为了实现有针对性的干预措施,以改善患者的坚持.
主要方法:
- 利用了来自295,758名健康应用用户的大规模数据集 (超过1.99亿条记录).
- 采用LightGBM模型,根据2周的测量模式和人口统计数据预测未来的不活动.
- 应用了SHAP分析来识别脱校的重要预测因素.
主要成果:
- 轻GBM模型实现了高预测准确度 (AUC为28天0.930,56天预测为0.851).
- 在SHAP分析中,在年轻/老年用户,女性和未报告性别的用户中发现了较高的脱学风险.
- 最大缩血压 (SBP) 和减少周日测量是显著的脱学预测因素.
结论:
- 开发的模型准确地预测了血压监测不活性的情况.
- 确定了新的风险因素,包括最大SBP和测量模式.
- 该模型支持早期,有针对性的干预措施,以提高监测遵守率和改善高血压管理.
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