LifeClock uses routine clinical data to model across infancy, childhood, adulthood, and old age.
Evidence
This model-development study used EHRFormer on 24,633,025 longitudinal clinical visits from 9,680,764 individuals to create pediatric and adult biological clocks tied to current and future disease risks.
Caveat
The abstract reports prediction and association from electronic health record data, so the claims depend on model performance and the clinical data used to build the latent representations.
Simplified
Aging research has primarily focused on adult aging clocks, leaving a critical gap in understanding a biological clock across the full life cycle, particularly during infancy and childhood. Here we introduce LifeClock, a biological clock model that predicts across all life stages using routine electronic health records and laboratory test data. To enhance individualized predictions, we integrated virtual patient representations from 24,633,025 heterogeneous longitudinal clinical visits across 9,680,764 individuals and projected them into a latent space. Our approach leverages EHRFormer, a time-series transformer-based model, to analyze developmental and aging dynamics with high precision and develop accurate biological age clocks spanning infancy to old age. Our findings reveal distinct biological clock patterns across different life stages. The pediatric clock is strongly associated with children's development and accurately predicts current and future risks of major pediatric diseases, including malnutrition, growth and developmental abnormalities. The adult clock is strongly associated with aging and accurately predicts current and future risks of major age-related diseases, such as diabetes, renal failure, stroke and cardiovascular diseases. This work therefore distinguishes pediatric development from adult aging, establishing a novel framework to advance precision health by leveraging routine clinical data across the entire lifespan.
Key numbers
24,633,025
Clinical Visits Used
Total number of longitudinal clinical visits analyzed.
4.14
Mean Absolute Error ()
achieved in external validation cohort.
Full Text
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