Wearable units seize steady physiological alerts at inhabitants scale. These streams, starting from coronary heart charge dynamics to sleep patterns, can reveal early physiological modifications earlier than signs seem. The bottleneck is now not knowledge assortment, however turning these alerts into dependable, clinically significant biomarkers.
Present language model-based agent programs automate components of the scientific workflow, however can typically break down on physiological time-series knowledge. These programs 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 beneath 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 recognized medical alerts, recognized convergent biomarkers throughout unbiased datasets, and improved downstream prediction when mixed with demographic options.
