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机器学习用于预测心力衰竭与高血压相结合的老年患者的住院死亡率:一项多中心回顾性研究

Xiaozhu Liu^1,2, Zulong Xie¹, Yang Zhang³

¹Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Cardiovascular diabetology

|November 16, 2024

概括

一个新的机器学习模型准确地预测了心力衰竭和高血压的老年患者的住院死亡率. 确定的主要危险因素包括尿素,中性粒细胞和白蛋白水平,有助于个性化风险评估.

科学领域:

老年医学老年医学
心脏病学心脏病学
医疗信息学医疗信息学

背景情况:

心力衰竭和高血压显著增加老年患者 (≥65岁) 的住院死亡风险.
在这个特定的人口群体中,对于住院死亡率存在有限的预测模型.
这项研究解决了老年患者同时存在心力衰竭和高血压的个性化风险评估的需要.

研究的目的:

开发和验证用于预测医院死亡率的机器学习模型.
确定与心力衰竭和高血压的老年患者住院死亡相关的关键临床风险因素.
加强个性化的患者护理和风险分层.

主要方法:

利用了4647名心力衰竭和高血压的老年患者的数据 (2012-2021年).
雇佣的最小绝对收缩和选择操作员 (LASSO) 用于变量选择.
评估了八个机器学习算法,根据曲线下的面积 (AUC) 选择了最佳模型.
应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) 来实现模型的可解释性.

主要成果:

随机森林模型表现出卓越的性能,AUC为0.850.
确定的住院死亡率的关键预测因素包括尿素,住院时间,中性粒细胞,白蛋白和高密度脂蛋白胆固醇.
该模型实现了高精度 (0.738),回忆 (0.837) 和特异性 (0.734).

结论:

一个强大的随机森林模型有效预测心力衰竭和高血压的老年患者的住院死亡率.
该研究成功确定了关键风险因素,使得临床决策更加精确.
这种机器学习方法为老年心脏病学中个性化风险评估和管理提供了有价值的工具.

关键词:

心脏衰竭是因为心脏衰竭.在高血压的高血压.机器学习机器学习死亡率死亡率死亡率预测模型是一个预测模型.

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