Mantis

Founded by Georgia Witchel

Create predictive digital twins of individuals using physiological measurements and digital interaction records

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You can use Mantis to generate living, predictive digital twins of individual people by connecting continuous physiological records and digital interactions. The system aggregates high-frequency telemetry, wearable data, and clinical history into forecasting models. Teams can query consented predictions to spot pre-symptomatic infections, track cognitive baseline decline, and forecast heart-failure decompensation risks. It also forecasts medication adherence, models post-operative recovery curves, and alerts providers before psychiatric relapses occur, giving developers and healthcare teams the ability to run targeted predictive interventions before conditions deteriorate.

What Mantis does

Ingest high-frequency smartwatch telemetry and digital recordsGenerate individualized physiological baseline modelsRequest scoped revocable access to predictive health metricsTrigger alerts when patient metrics diverge from expected recovery curvesSimulate clinical trial control groups using synthetic twinsForecast cardiac decompensation risk windows for at-risk patientsIngests continuous wearable telemetry and digital activity dataBuilds individualized predictive models representing physiological baselinesConsented data access architecture functioning similarly to OAuth permissionsFlags pre-symptomatic viral infections up to nine days earlyTracks cognitive drift over time to detect early baseline shiftsForecasts medication adherence gaps before treatment failures occurPredicts post-operative recovery paths to alert on deviations

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