Extracting biological age from biomedical data via deep learning: too much of a good thing?
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 oneweek
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-theart
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-thebox
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.