ArticleAging cell2023
Explainable machine learning framework to predict personalized physiological aging.
Article in Aging cell, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed.
- An interpretable machine learning model for predicting febrile seizures following enterovirus infection in children.Annals of medicine · 2026Article
- Comprehensive assessment of minors' biological profiles using explainable machine learning: the assessment of age, stature, body mass, and sex.International journal of legal medicine · 2026Article
- Integrating Environmental Exposure Profiles with Temporal Transcriptomics: An Explainable Machine Learning Model for Cardiovascular Disease Risk Prediction and Biological Validation.Cardiovascular drugs and therapy · 2026Article
- Explainable Machine Learning Reveals Pattern-Specific Drivers of Depression in Middle-Aged and Older Adults with Multimorbidity: A Nationwide Cross-Sectional Study from CHARLS.Healthcare (Basel, Switzerland) · 2026Article
- Article
- Explainable and visualizable machine learning model development and validation for 5-year postoperative survival prediction in prostate cancer patients aged ≥ 65 years.BMC geriatrics · 2026Article
- A machine learning-derived biological age model for liver grafts provides a superior assessment of aging compared to chronological age in transplantation.Scientific reports · 2026Article
- An interpretable machine learning model for early risk stratification of medium-to-giant coronary artery aneurysm in Kawasaki disease: development and external validation.Pediatric research · 2026Article
- An interpretable machine learning approach using nnU-Net-based radiomics for preoperative risk stratification of thymic epithelial tumors: a multicenter study.BMC medical imaging · 2026Article
- Research on fall prediction in elderly patients with chronic diseases based on explainable machine learning: an aging perspective.BMC geriatrics · 2026Article
- Explainable machine learning for depression risk prediction in adults with obesity: development of an online tool.BMC medical informatics and decision making · 2026Article
- The Burden and Projections for 2036 of Periodontal Diseases in Asia From 1990 to 2021.International dental journal · 2026Article
- CardioMetAge estimates cardiometabolic aging and predicts disease outcomes.BMC medicine · 2026Article
- FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- Decoding the tumor-aging axis: from bench to clinical.Frontiers in immunology · 2026Review
- An interpretable prediction model for non-invasive identification of the phlegm-dampness constitution based on machine learning.Frontiers in endocrinology · 2026Article
- Development and Validation of a Machine Learning Model to Predict the Risk of Medical Decision-Making Delay in Acute Myocardial Infarction Patients From Multicenter Tertiary Hospitals in China.International journal of general medicine · 2026Article
- Establishment and validation of a CT-based clinical deep learning radiomics nomogram for predicting the response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Development of an online prediction tool for immunotherapy-related adverse events in patients with advanced NSCLC based on machine learning and temporal validation.Frontiers in oncology · 2026Article
- Relationships among dietary patterns and heterogeneous biological aging at system and organ-specific levels and mortality risks.NPJ science of food · 2025Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
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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