评估EMR ML采矿方法用于测量金属混合物与高血压死亡率之间的关联
1Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China.
概括
这项研究开发了一种机器学习模型,用于预测暴露于重金属的高血压患者的死亡率. 尿液和血液中的特定金属被确定为危险因素,有助于患者风险评估.
科学领域:
- 环境健康 环境健康
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 关于重金属暴露与高血压患者死亡风险之间的关联的数据有限.
- 高血压是一种普遍的疾病,了解环境风险因素对于患者的治疗结果至关重要.
研究的目的:
- 开发一种可解释,高效和强大的机器学习模型,以根据重金属暴露来预测高血压患者的死亡率.
- 识别与增加或减少死亡风险相关的特定重金属及其生物矩阵 (尿液,血液).
主要方法:
- 利用了来自美国国家健康和营养检查调查 (NHANES, 2013-2018) 的数据,其中包括2347名参与者.
- 开发和优化了五个机器学习模型,选择了通过遗传算法 (GA) 增强的极端梯度增强 (XGB) 模型.
- 采用了夏普利添加式解释 (SHAP) 和局部可解释的模型不可知解释 (LIME) 来实现模型可解释性和特征可视化.
主要成果:
- 该GA-XGB模型在预测死亡率方面实现了高性能,AUC为0.959,准确度为96.8%.
- 与死亡率的积极关联与尿,,,,血和有关.
- 观察到尿路,,,锡,,血和的负面关联.
结论:
- 一个可解释的GA-XGB模型有效地预测了高血压患者因重金属暴露而导致的死亡率.
- 确定了关键的尿路和血液重金属,这些重金属与死亡风险具有积极和消极的相关性,为有针对性的干预提供了洞察力.
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