nhanes

该论文提出一个临床可解释的机器学习框架，使用CatBoost和自动化的SHAP依赖图定量分析，从NHANES的44个异构临床特征中估计生物年龄。研究发现模型估计的生物年龄与实际年龄高度相关，并识别出血尿素氮、血糖和剧烈运动等关键预测因子。

CardioMeta is a calibrated multi-task framework for jointly predicting diabetes, hypertension, and cardiovascular disease across NHANES and MIMIC-IV data, emphasizing leakage control, calibration, and transparent reliability.

This paper introduces the NHANES Accelerometry Cardiometabolic Benchmark, a population-representative tabular dataset for predicting cardiometabolic risk from accelerometry data, and evaluates ridge regression, XGBoost, and TabPFN v2 with uncertainty quantification using conformal prediction.