用真实人群数据构建运动传感器生物标志物基准，评估预测糖尿病风险的准确性。
Accelerometry-Derived Digital Biomarkers for Cardiometabolic Risk: A Population-Representative Tabular Benchmark with Uncertainty Quantification
- 基于全国代表性人群数据，用加速度计和生活方式变量预测代谢疾病指标
- 表型模型对血糖和炎症因子预测效果较好（R²最高达0.383），血脂预测仍不理想
- 引入不确定性量化方法，揭示不同人种性别在预测公平性上的差距
结构化表格数据主导临床医学，但现有基准无法反映复杂抽样、人口过度采样及子群公平性等真实世界特性。本文基于NHANES 2003–2006数据，构建了包含1,381名成人的加速度计-代谢风险基准，涵盖髋部佩戴加速度计数据、空腹生化指标、膳食摄入与体格测量。评估了岭回归、XGBoost和基础模型TabPFN v2三种表格学习方法，用于从活动表型和生活方式协变量预测糖化血红蛋白（HbA1c）、空腹甘油三酯和超敏C反应蛋白（CRP）。TabPFN v2整体表现最佳（HbA1c R²=0.156，CRP R²=0.383），而甘油三酯预测效果差（R² < 0.05），与已知遗传主导性一致。采用分层合取预测生成无分布假设的90%预测区间，并评估性别与种族/族裔子群的覆盖公平性。总体覆盖接近90%目标（CRP与HbA1c），但甘油三酯偏低；子群层面存在局部覆盖不足（如墨西哥裔人群的HbA1c预测），凸显边际保证与临床公平所需条件覆盖之间的差距。代码与数据见https://github.com/felizzi/nhanes-accel-cardiometabolic-benchmark。
原文摘要 · Abstract (English)
Structured tabular data dominates clinical medicine, yet existing benchmarks fail to reflect real-world properties like complex survey sampling, demographic oversampling, and subgroup fairness. We introduce the NHANES Accelerometry Cardiometabolic Benchmark, derived from NHANES 2003-2006, comprising 1,381 adults with hip-worn accelerometry, fasting laboratory biomarkers, dietary intake, and anthropometrics. We evaluate three tabular learning methods -- ridge regression, XGBoost, and the foundation model TabPFN v2 -- to predict glycated haemoglobin (HbA1c), fasting triglycerides, and C-reactive protein (CRP) from activity phenotypes and lifestyle covariates. TabPFN v2 achieves the best overall performance (HbA1c R^2=0.156, CRP R^2=0.383), while triglycerides remain largely unpredictable (R^2 < 0.05), consistent with known genetic dominance. We apply split conformal prediction to generate distribution-free 90% prediction intervals and evaluate demographic coverage equity across sex and race/ethnicity subgroups. Marginal coverage aligns with the 90% target for CRP and HbA1c but falls below for triglycerides. At the subgroup level, we observe localized undercoverage (e.g., HbA1c for Mexican American participants), illustrating the gap between marginal guarantees and the conditional coverage required for clinical fairness. Code and data are at https://github.com/felizzi/nhanes-accel-cardiometabolic-benchmark.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。