A concise whitepaper framing DT4H as longitudinal infrastructure for cohorts, reference humans, Twins, calibration, StateK, SETPOINT, and governed runtime intelligence.
DT4H is Digital Twin for Health infrastructure. It is designed to model longitudinal health context using cohorts, reference-human priors, individualized Twin state, calibration events, confidence, and runtime feedback. DT4H is not a diagnostic system; it is an infrastructure layer that supports bounded state computation and governed execution workflows.
The system connects DT4H modeling with StateK computation and SETPOINT execution. StateK converts calibrated Twin state into current state, confidence, trajectory, and transition readiness. SETPOINT turns bounded state into protocols, practices, check-ins, outcomes, and recalibration feedback.
Cohorts · reference humans · Twins · calibration
State · confidence · trajectory · readiness
Protocols · practices · outcomes · feedback
Validation · review · non-diagnostic use
Health data is fragmented across records, visits, devices, and workflows.
DT4H models longitudinal state through cohorts, priors, and calibrated Twins.
StateK produces confidence, trajectory, readiness, and transition logic.
SETPOINT closes the loop through practices, protocols, and outcomes.
Clinical interpretation remains clinician-governed and non-autonomous.
Pilots evaluate runtime observability, workflow fit, and longitudinal evidence.