为低收入人口开发护士可访问的高血压预测工具:用SHAP解释机器学习算法的比较分析
Chuan Huang1, Jiaojiao Xu2, Hai Qiu3
1School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
International journal of nursing practice
|September 23, 2025
概括
机器学习模型有效地预测低收入群体的高血压,神经网络显示最佳性能. 一个护士友好的名录图有助于风险评估,在资源有限的环境中改善高血压查.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 公共卫生研究 公共卫生研究
背景情况:
- 高血压是一个重要的公共卫生问题,特别是在低收入人群中.
- 准确的预测模型对于早期检测和管理至关重要.
- 资源有限的环境需要可解释的工具来实现护理.
研究的目的:
- 开发和比较用于高血压预测的机器学习算法.
- 强调在资源有限的环境中用于护理的模型可解释性.
- 确定低收入人群中高血压的关键预测因素.
主要方法:
- 对来自低收入人口的NHANES数据 (2005-2018) 的回顾性分析.
- 开发和评估八种机器学习模型.
- 应用SHAP方法论用于模型解释和临床名录创建.
主要成果:
- 12,506名参与者的39.96%患有高血压.
- 物流回归和神经网络实现了最高的预测准确性 (AUC=0.853).
- 年龄,腰围和糖尿病状况是关键预测因素;开发了一个风险分层的名录图.
结论:
- 使用SHAP解释的神经网络模型提供最佳,透明的高血压预测.
- 开发的nomogram为护理评估提供了一个视觉评分系统.
- 该工具支持低收入人群的基于证据的查,需要外部验证.
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