医学
危险系数
比例危险模型
心力衰竭
内科学
心脏病学
预测建模
置信区间
机器学习
计算机科学
作者
Ke Wang,Jing Tian,Chu Zheng,Hong Yang,Jia Ren,Yanling Liu,Qinghua Han,Yanbo Zhang
标识
DOI:10.1016/j.compbiomed.2021.104813
摘要
The ML-based risk stratification tool was able to accurately assess and stratify the risk of 3-year all-cause mortality in patients with HF caused by CHD. ML combined with SHAP could provide an explicit explanation of individualized risk prediction and give physicians an intuitive understanding of the influence of key features in the model.
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