CardioMeta：跨人群和电子健康记录数据的糖尿病、高血压和心血管疾病校准多任务预测
CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data
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中文总结 AI 辅助
研究针对糖尿病、高血压和心血管疾病常共发且有共同决定因素的情况，提出CardioMeta校准多任务框架，结合共享编码器、特定门控头和概率校准，在多数据源联合预测中控制泄漏、校准概率，展现多任务建模在可靠性报告等方面的价值。
中文摘要 AI 辅助
心脏代谢疾病仍然是可预防发病的最持久驱动因素之一，因为糖尿病、高血压和心血管疾病经常同时出现并共享代谢、血管、人口统计学和行为决定因素。现有用于慢性病预测的机器学习研究往往强调在单个数据集上的区分，而对标签泄漏、校准、时间鲁棒性、外部可迁移性和亚组可靠性的报告不足。本文提出了CardioMeta，这是一个用于跨人群调查和电子健康记录（EHR）数据联合预测糖尿病、高血压和心血管疾病的校准多任务框架。该研究使用NHANES进行人群水平的模型开发和时间验证，并使用MIMIC-IV在显著分布转移下进行EHR领域评估。为了减少循环标签重建，主要分析从相应的预测头中排除疾病定义变量，而仅保留全临床特征设置作为敏感性分析。CardioMeta将共享的心脏代谢编码器与疾病特定的门控头和事后概率校准相结合。在减少泄漏的时间验证设置中，该模型实现了0.839的宏AUROC、0.536的宏AUPRC、0.614的宏F1和0.024的预期校准误差，比强大的梯度提升和神经表格基线有适度但一致的改进。在MIMIC-IV上的外部评估显示在域转移下性能明显下降，而有限的微调部分恢复了性能。研究结果表明，多任务心脏代谢建模的主要价值不在于提高准确性，而在于可重复的泄漏控制、校准概率以及跨异构医疗数据源的透明可靠性报告。
英文摘要
Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular disease frequently co-occur and share metabolic, vascular, demographic, and behavioral determinants. Existing machine learning studies for chronic disease prediction often emphasize discrimination on a single dataset, while underreporting label leakage, calibration, temporal robustness, external transportability, and subgroup reliability. This paper presents CardioMeta, a calibrated multi-task framework for joint prediction of diabetes, hypertension, and cardiovascular disease across population survey and electronic health record (EHR) data. The study uses NHANES for population-level model development and temporal validation, and MIMIC-IV for EHR-domain evaluation under substantial distribution shift. To reduce circular label reconstruction, the primary analysis excludes disease-defining variables from the corresponding prediction heads, while a full-clinical feature setting is retained only as sensitivity analysis. CardioMeta combines a shared cardiometabolic encoder with disease-specific gated heads and post-hoc probability calibration. In the leakage-reduced temporal validation setting, the model achieved a macro-AUROC of 0.839, macro-AUPRC of 0.536, macro-F1 of 0.614, and expected calibration error of 0.024, with modest but consistent improvements over strong gradient-boosting and neural tabular baselines. External evaluation on MIMIC-IV showed clear degradation under domain shift, while limited fine-tuning partially recovered performance. The findings indicate that the principal value of multi-task cardiometabolic modeling lies not in inflated accuracy, but in reproducible leakage control, calibrated probabilities, and transparent reliability reporting across heterogeneous healthcare data sources.
发表机构
- School of Computing, Wichita State University（威奇托州立大学计算学院）
- Department of Computer Science and Engineering, American International University-Bangladesh（孟加拉国美国国际大学计算机科学与工程系）
- School of Computer Science and Engineering, The University of Aizu（会津大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
