在线机器学习模型用于预测高血压患者的药物坚持:来自中国健康与退休纵向研究 (CHARLS) 的数据
Hanxu Zhang1, Minxue Sun2, Xiaoran Hou1
1Department of Pharmacy, Tianjin Medical University General Hospital, No.154, Anshan Road, Heping District, Tianjin, 300052, China.
International journal of clinical pharmacy
|February 17, 2026
概括
这项研究开发了一种可解释的机器学习模型,用于预测中国高血压患者的药物坚持. 该工具提供个性化的风险评估,帮助针对性干预,以更好地管理高血压.
科学领域:
- 机器学习在公共卫生中的应用.
- 心血管疾病的研究研究.
- 药物经济学和坚持性研究.
背景情况:
- 高血压是全球主要的死亡原因,控制率不佳.
- 药物不服药是管理高血压的一个重大挑战.
- 现有的预测模型缺乏中国高血压患者的国家代表性和多因素纳入.
研究的目的:
- 开发一种可解释的机器学习模型,用于预测中国高血压患者的药物坚持.
- 确定影响该群体药物坚持的关键因素.
- 为风险分层和干预计划创建一个用户友好的工具.
主要方法:
- 利用了来自中国健康和退休长度研究 (CHARLS) 的数据,其中有超过2700名参与者.
- 采用随机森林用于缺失数据的归算和LASSO回归用于特征选择.
- 训练和评估了七个机器学习算法,其中XGBoost表现最好.
- 使用Shapley添加式解释 (SHAP) 解释模型预测,并开发了一个基于Shiny的Web应用程序.
主要成果:
- 在45岁以上的中国高血压患者中,超过53%的患者表现出药物坚持率较低.
- 在XGBoost模型中,AUC达到0.828,准确度为0.726,F1得分为0.713.
- SHAP分析确定了诸如多种慢性疾病,肥胖,老年和城市居住等因素与更好的遵守有关,而吸烟和就业与不遵守有关.
结论:
- 一个可解释的XGBoost模型和一个在线工具已成功开发,用于预测中国高血压患者的药物坚持.
- 该工具为有针对性的干预提供了可操作的,透明的风险分层.
- 建议使用电子医疗记录或客观合规数据进行外部验证,以提高概括性.
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