融合机器学习与生存分析，提升慢性肾病风险分层预测能力
Integrated Machine Learning and Survival Analysis Modeling for Enhanced Chronic Kidney Disease Risk Stratification
- 用多种机器学习模型挖掘新预测因子，结合Shapley值评估重要性
- 整合新旧特征后，在Cox模型中显著提升对肾衰进展的预测效能
- 适合临床风险评估与早期干预研究者参考
慢性肾病（CKD）是重大公共卫生挑战，若未及早发现和管理，常进展为终末期肾病（ESRD）。由于疾病进展隐匿，早期干预可显著降低发病率、死亡率及经济负担。本研究提出一种新方法，结合机器学习与经典统计模型建模CKD进展。基于Liu等（2023）的工作，我们评估了线性模型、树模型和深度学习模型，以提取新的预测因子，并使用Shapley值评估特征重要性。将这些新识别的预测因子与现有的肾衰风险方程（Kidney Failure Risk Equation）中的临床特征整合，应用于Cox比例风险模型，以预测CKD进展。
原文摘要 · Abstract (English)
Chronic kidney disease (CKD) is a significant public health challenge, often progressing to end-stage renal disease (ESRD) if not detected and managed early. Early intervention, warranted by silent disease progression, can significantly reduce associated morbidity, mortality, and financial burden. In this study, we propose a novel approach to modeling CKD progression using a combination of machine learning techniques and classical statistical models. Building on the work of Liu et al. (2023), we evaluate linear models, tree-based methods, and deep learning models to extract novel predictors for CKD progression, with feature importance assessed using Shapley values. These newly identified predictors, integrated with established clinical features from the Kidney Failure Risk Equation, are then applied within the framework of Cox proportional hazards models to predict CKD progression.
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