ArticleScientific reports2018
Extracting biological age from biomedical data via deep learning: too much of a good thing?
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed.
- Wearable-derived skin temperature dynamics during sleep reveal cardiovascular perfusion deficits through mechanistic modeling.NPJ digital medicine · 2026Article
- Molecular markers, mechanisms and metrics of biological aging: a scoping review.Frontiers in aging · 2026Review
- AI in Neurology: Everything, Everywhere, all at Once PART 2: Speech, Sentience, Scruples, and Service.Annals of neurology · 2025Review
- Hybrid Attention-Enhanced Xception and Dynamic Chaotic Whale Optimization for Brain Tumor Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Age Estimation From Blood Test Results Using a Random Forest Model.Journal of clinical laboratory analysis · 2025Article
- Biological age prediction and NAFLD risk assessment: a machine learning model based on a multicenter population in Nanchang, Jiangxi, China.BMC gastroenterology · 2025Article
- EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification.Frontiers in oncology · 2025Article
- Metabolomic age (MileAge) predicts health and life span: A comparison of multiple machine learning algorithms.Science advances · 2024Article
- Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention.Nature aging · 2024Article
- Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks.International journal of molecular sciences · 2024Review
- A novel approach to quantifying individual's biological aging using Korea's national health screening program toward precision public health.GeroScience · 2024Article
- Review
- An evaluation of aging measures: from biomarkers to clocks.Biogerontology · 2023Review
- Progress in biological age research.Frontiers in public health · 2023Review
- Machine learning approaches to predict age from accelerometer records of physical activity at biobank scale.PLOS digital health · 2023Article
- Measuring biological age using omics data.Nature reviews. Genetics · 2022Review
- Factors Affecting the Usage of Wearable Device Technology for Healthcare among Indian Adults: A Cross-Sectional Study.Journal of clinical medicine · 2022Article
- A machine learning-based data mining in medical examination data: a biological features-based biological age prediction model.BMC bioinformatics · 2022Article
- Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness.PLOS digital health · 2022Review
- Predicting physiological aging rates from a range of quantitative traits using machine learning.Aging · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Age-related physiological changes in humans are linearly associated with age. Naturally, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biological age. In this work, we used one-week long physical activity records from a 2003-2006 National Health and Nutrition Examination Survey (NHANES) to compare three increasingly accurate biological age models: the unsupervised Principal Components Analysis (PCA) score, a multivariate linear regression, and a state-of-the-art deep convolutional neural network (CNN). We found that the supervised approaches produce better chronological age estimations at the expense of a loss of the association between the aging acceleration and all-cause mortality. Consequently, we turned to the NHANES death register directly and introduced a novel way to train parametric proportional hazards models suitable for out-of-the-box implementation with any modern machine learning software. As a demonstration, we produced a separate deep CNN for mortality risks prediction that outperformed any of the biological age or a simple linear proportional hazards model. Altogether, our findings demonstrate the emerging potential of combined wearable sensors and deep learning technologies for applications involving continuous health risk monitoring and real-time feedback to patients and care providers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.