机器学习
公制(单位)
亚临床感染
糖化血红素
计算机科学
变量(数学)
全国健康与营养检查调查
人工智能
人口
限制
估计
生物
统计
数学
糖尿病
医学
2型糖尿病
环境卫生
数学分析
病毒学
经济
内分泌学
工程类
管理
机械工程
运营管理
作者
David Bernard,Emmanuel Doumard,Isabelle Ader,Philippe Kémoun,Jean‐Christophe Pagès,Anne Galinier,Sylvain Cussat‐Blanc,Félix Furger,Luigi Ferrucci,Julien Aligon,Cyrille Delpierre,Luc Pénicaud,Paul Monsarrat,Louis Casteilla
出处
期刊:Aging Cell
[Wiley]
日期:2023-06-10
卷期号:22 (8)
被引量:2
摘要
Abstract Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter‐parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty‐six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age‐specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow‐up. These data show that PPA is a robust, quantitative and explainable ML‐based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.
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