The architecture separates signals, cohort intelligence, reference-human priors, Twin initialization, calibration, StateK computation, and SETPOINT execution.
Biomarkers · behavior · clinical · self-report
Cohorts · reference humans · Twin initialization · calibration
State · confidence · trajectory · transition logic
Protocols · practices · outcomes · feedback
DT4H is designed as an infrastructure layer, not an end-user wellness app. Each layer has a bounded responsibility so that modeling, state computation, and execution remain separable.
Normalizes biomarkers, behavior, clinical context, and self-report.
Resolves probabilistic population context for inference and initialization.
Provides distributional priors, expected ranges, and variance envelopes.
Creates and maintains the individualized longitudinal model.
Computes state, confidence, trajectory, and transition readiness.
Feeds SETPOINT protocols, practices, outcomes, and recalibration.
Evidence enters the platform as normalized runtime signals.
Cohorts, reference humans, initialization, and calibration.
Current state, confidence, transition logic, and readiness.
Protocols, practices, outcomes, and feedback.
DT4H outputs are computational modeling signals, not clinical conclusions.
StateK computes readiness and transition logic, while SETPOINT handles execution.
Outcomes from protocols and practices return as evidence for recalibration.