开发基于机器学习的模型,用于预测个体对抗高血压治疗的反应
Jiayi Yi1, Lili Wang1, Jiali Song1
1National Clinical Research Center for Cardiovascular Diseases, NHC Key Laboratory of Clinical Research for Cardiovascular Medications, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China.
机器学习模型可以预测个人对高血压药物的血压反应. 这有助于临床医生选择最佳治疗方法,以更好地控制血压.
科学领域:
- 心血管医学 心血管医学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 个性化的抗高血压药物选择对于有效的高血压管理至关重要.
- 预测个体患者对药物的反应是一个关键的挑战.
研究的目的:
- 开发和评估一种机器学习 (ML) 模型,用于预测个人对抗高血压药物的血压反应.
- 确定影响血压反应的关键临床特征.
主要方法:
- 利用了实用性,集群随机试验的数据,涉及19013次高血压管理访问 (6282名患者).
- 采用最小绝对收缩和选择运算符 (LASSO) 方法进行特征选择.
- 开发并比较了ML模型,其中极端梯度提升 (EGB) 显示出最佳性能.
主要成果:
- 选择了12个表型特征和药物数据来预测随后的BP.
- 在试验组中,EGB模型实现了8.57mmHg的平均绝对误差 (MAE) 和0.28的R平方.
- 使用MAE和R平方在随机划分的训练/测试数据集 (7:3比率) 上评估模型性能.
结论:
- 机器学习模型在预测个体抗高血压治疗反应方面显示出显著的潜力.
- 这种方法可以帮助临床医生安全有效地优化血压控制.
- 开发的ML模型可以帮助个性化高血压管理.
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