Wearable gadgets seize steady physiological indicators at inhabitants scale. These streams, starting from coronary heart fee dynamics to sleep patterns, can reveal early physiological adjustments earlier than signs seem. The bottleneck is now not information assortment, however turning these indicators into dependable, clinically significant biomarkers.
Present language model-based agent techniques automate components of the scientific workflow, however can usually break down on physiological time-series information. These techniques optimize for predictive efficiency whereas overlooking statistical validity, resulting in spurious correlations, leakage, and brittle options.
To this finish, we introduce the Biomarker Discovery Framework, a multi-agent system that buildings candidate biomarker prioritization as an iterative analysis loop below human supervision. By combining speculation technology, parallel statistical evaluation, mannequin coaching, adversarial validation, and literature-grounded reasoning, Biomarker Discovery Framework accelerates the invention course of whereas sustaining strict statistical rigor and preserving human oversight. Throughout three cohorts (N = 9,279 participant-observations), Biomarker Discovery Framework recovered identified scientific indicators, recognized convergent biomarkers throughout unbiased datasets, and improved downstream prediction when mixed with demographic options.
