建立基于机器学习的死亡率的手握强度值在中国人口的潜在队列使用机器学习
Haofeng Zhou1, Zepeng Chen2, Yuting Liu3
1Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Frontiers in medicine
|December 18, 2023
概括
手握强度 (HGS) 是死亡率的关键预测因素,其表现优于其他风险因素. 新的特定性别的HGS值提高了健康查准确度,以识别高死亡风险的个人.
科学领域:
- 老年学是指老年学的学科.
- 公共卫生 公共卫生
- 生物统计学 生物统计学
背景情况:
- 与其他死亡风险因素相比,手握强度 (HGS) 的预后值需要澄清.
- 缺乏在健康查中通过HGS识别高风险个人的确立门.
研究的目的:
- 通过机器学习研究HGS的预后重要性.
- 为有效的健康查建立性别特定的HGS值.
主要方法:
- 利用了来自中国健康与退休长度研究 (CHARLS) 的数据,其中有6,762名参与者.
- 采用随机森林模型来确定所有原因死亡率的预后变量.
- 应用了夏普利添加剂解释 (SHAP) 和考克斯模型进行验证.
主要成果:
- 手握强度 (HGS) 被确定为第五个最重要的预后变量.
- 已确立的性别特定门:男性<32公斤,女性<19公斤.
- 低HGS (男性<32公斤,女性<19公斤) 显著预测了全因死亡率 (HR: 1.77) 和改善了风险评分预测 (C指数变化: 0.022).
结论:
- 低握力 (HGS) 是所有原因死亡率的强有力的预测因素,超过其他风险因素.
- 建立的特定性别的HGS值提高了传统风险评分的预测准确性.
- 测量HGS对于健康查和风险分层具有临床价值.
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