Setting the Canvas
We are individually shaped over time through the experiences and environments we come in contact with. This sum of experiences determines how we will react to future environments.
Our ambition is to identify how the accumulation of experiences will project into the future and through this be better equipped to estimate how given interventions or lifestyle changes will effect us individually.
Generating Enough Data
Predictive Models for the Individual
Machine learning relies on big datasets that work to teach the programs what are best predictions for given circumstances.
On the personal level we do not record sufficient amount of reliable data that could be used to accurately teach a program good enough models to predict on the individual level.
We use a combination of scripted default behaviours derived from general population data with artificial intelligence to learn how each individual differs from the mean.
Viewing Each as an Individual
Previsualize Changes Over Time
By creating a model that is tuned to fit personal data. Our model is able to visualise how given interventions or lifestyle changes might effect each individual in the long-run and short term.
This also allows to set a desired outcomes and the algorithms will calculate what changes will be likely to get to the set goal.
Common Point Contextualisation and Synchronisation
Health and Fitness Data Pool
Consolidate health data from your health practitioner, fitness tracker and connect your favourite apps you already use. Pooling more data will provide better personal predictive models to help keep track and visualise your progress in one convenient place.
Visualise how important biomarkers interact and how they effect each other.
Content Management System for Health Data
The node based health data editor is a powerful tool that helps medical researchers account for a greater number of variables in their research opening the possibility to explore given datasets from numerous contexts and perspectives.
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“Man should not try to avoid stress any more than he would shun food, love or exercise.”