DT4H does not treat users as isolated records. It models individuals relative to structured populations, reference priors, and continuous calibration loops.
Multi-dimensional population context used for inference, comparison, and initialization.
Cohort-derived distributions that define expected ranges, variance, trajectories, and priors.
Continuous model refinement as signals, outcomes, and runtime evidence accumulate.