目的 分析基于机器学习的慢性肾脏病死亡风险预测模型在慢性肾脏病相关领域应用的现有证据。 方法 在万方医学网、中国知网、维普网、Pubmed、Embase、web of science 6个数据库，使用主题词结合关键词和布尔逻辑运算符连接词结合的方式进行检索。纳入文章包含一种或以上的基于人工智能的预测模型。 结果 检索后共获得918篇文章，最终15篇文章被纳入。基于人工智能的风险预测模型（随机森林、支持向量机、极端梯度提升）使用率最高（n=11,73.3%），该类风险预测模型的效果更好，其中使用极端梯度提升模型的AUC均大于0.8（95%CI：0.768～0.832）。结论 基于机器学习的预测模型在预测慢性肾脏病患者死亡风险方面通常比传统的预测方法效果更好。。

Objective  The purpose of this article was to analyze the existing evidence on the application of machine learning-based chronic kidney disease mortality risk prediction models in chronic kidney disease-related fields.  Methods  Literature search was conducted using subject words combined with free words in six databases: Wanfang Medicine, China National Knowledge Infrastructure, VIP, Pubmed, Embase, and Web of Science. The included articles contain one or more prediction models based on artificial intelligence.  Result  A total of 918 articles were retrieved, and ultimately 15 articles were included in the analysis. The usage rate of artificial intelligence-based risk prediction models (random forest, support vector machine, and extreme gradient boosting) was the highest (n=11, 73.3%), and the performance of this type of risk prediction model was better. Among them, the AUCs of the extreme gradient boosting models were all greater than 0.8 (95% CI: 0.768～0.832).  Conclusion Prediction models based on machine learning are generally better than traditional prediction methods in predicting death risk in patients with chronic kidney disease.