Estimating biological age from routine records with LifeClock
AI model LifeClock reads routine health records to estimate biological age, predicting disease risk years ahead to inform prevention.
LifeClock, reported in Nature Medicine, estimates biological age from routine health records. Trained on 24.6 million visits and 184 indicators, it offers a low-cost route to precision health beyond simple chronology.
Researchers found two distinct clocks: a paediatric development clock and an adult ageing clock. Specialised models improved accuracy, reflecting scripted growth versus decline. Biomarkers diverged between stages, aligning with growth or deterioration.
LifeClock stratified risk years ahead. In children, clusters flagged malnutrition, developmental disorders, and endocrine issues, including markedly higher odds of pituitary hyperfunction and obesity. Adult clusters signalled future diabetes, stroke, renal failure, and cardiovascular disease.
Performance was strong after fine-tuning: the area under the curve hit 0.98 for current diabetes and 0.91 for future diabetes. EHRFormer outperformed RNN and gradient-boosting baselines across longitudinal records.
Authors propose LifeClock for accessible monitoring, personalised interventions, and prevention. Adding wearables and real-time biometrics could refine responsiveness, enabling earlier action on emerging risks and supporting equitable precision medicine at the population scale.
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