However, it also tends to be siloed, unstructured, static, and even too abundant for human minds alone to optimally manage and process. As a result, a massive amount of it is not adequately translating to improved health outcomes.
Many of these diseases develop while "signaling" themselves through non-molecular pathways, depending on the disease, typically as subtle, occasional, or intermittent physiological or behavioral "signs." As humans, we subsequently observe these signs as symptoms, often later in a disease's stage, and sometimes too late.
We define LIFEdata as sensor-measured information digitally generated from life and around life. We are connecting LIFEdata as learnable insights from expressions & environment to disease detection and prediction through machine learning and artificial intelligence.
Many developing diseases have a dynamic pattern of progression that can be more timely detected through continuous physiological and behavioral measures. Timelier intervention, catalyzed by detection, will improve health and cost outcome ROI "return on (earlier) intervention".
Consumer smart devices with sensors that can capture LIFEdata are starting to rapidly populate our Life settings. These digital devices provide individuals with the opportunity to contribute to and access novel healthcare innovations outside the Health setting. Even IoT home devices not originally purposed for healthcare applications can be a very valuable source of LIFEdata which will be processed by our technology platform. We believe subtle and quantifiable, non-molecular and "digitizable" signals are under-discovered, but very insightful objective "markers" for many developing diseases and disorders.
Smartphones alone provide the Consumer a number of sensory modalities to passively generate LIFEdata. New chips, hardware, and sensors coming to market will turn the smart devices in our Life setting into health tools.
We have only just begun to scratch the surface with the number of possibilities: n! / r! * (n − r)! ...
We begin with the premise that every signal from life has the potential to be a "biomarker" weighted from 0 to 1, not 0 and 1.
LIFEdata can become a digital biomarker when a relationship is drawn to a health-related clinical diagnostic or outcome.
We are developing digital biomarkers by algorithmically training BioEngine4D via machine learning and artificial intelligence — premised around clinically-known pathophysiologies.
As our users aggregate more types of LIFEdata, they can begin to serve as a central data portal and reposition themselves closer to the center of the healthcare paradigm. We are developing BioEngine4D to enable us to digitally detect disease signs that are currently too subtle for an individual's or doctor's observational abilities, leading to more timely intervention.
Our solution is largely predicated on the novelty, complexity, and accuracy of BioEngine4D. Moreover, BioEngine4D becomes maximally commercializable with a front-end App layer that is versatile for both Consumers or Health professionals, intuitive to use, and value-additive to adopt.
Solutions that will make us realize our health had been in the dark 99% of the time in retrospect.