From health signals to
longevity models
HealthGen turns wearable, metabolic, and patient-reported signals into trusted health trajectories for AI, analytics, and longevity models.
Healthcare generates vast amounts of patient data. Almost none of it trains a model.
Without labels — patient context, intervention delivered, response window, measured outcome — data supports monitoring. With them, it supports learning. HealthGen turns fragments into consistent longitudinal trajectories: reconciled, time-aligned, and model-ready.
How it works
From raw events to model-ready trajectories
Ingest
Connect wearables, CGMs, food logs, surveys, medication apps, and clinical exports.
Reconcile
Adjudicate across sources, align to clinical time, and resolve duplicates, late arrivals, and timezone shifts.
Model
Serve model-ready trajectories and features to AI, dashboards, nudges, and longevity models.
Randomised controlled trial across SingHealth polyclinics
Intervention group at 12 months vs. control
Built through research, heading toward longevity
Longevity models are longitudinal models of healthspan trajectories. They estimate how a person's health state is evolving — not just snapshots, but trends across movement, sleep, metabolism, adherence, and clinical risk.
Programme infrastructure
Patient apps, wearable sync, food logs, surveys, dashboards, care workflows, and trial governance.
Personalised nudging
Behavioural nudges, profile-based intervention logic, and wearable-triggered feedback loops.
Federated learning
Federated recommendation layer with local training, aggregation, and governance controls across institutions.
Longevity models
ComingTrajectory models using behavioural, metabolic, patient-reported, and clinical signals to estimate healthspan.
Ready to turn health signals into longevity models?
See how HealthGen can help your team build on trusted trajectories instead of raw device exhaust.