However, it also tends to be siloed, unstructured, intermittent, and too multivariate 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.
LIFEdata will provide more objective, convenient, affordable, and frequent insights into our dynamic health.
We define LIFEdata as sensor-measured information digitally generated from life and around life. We are connecting LIFEdata as learnable insights from expressions and environment to disease detection and drug response through machine learning and artificial intelligence.
No! The signs of many diseases are not solely molecular, but can and should be measured non-molecularly as well. LIFEdata can be generated without needing to physically "touch" an intermediary (such as blood or tissue) – yielding unprecedented scalability – a key hallmark of the digital revolution.
Once a year or intermittent medical visits are ineffective. Many developing diseases have a dynamic pattern of progression that can be more timely detected through more frequent physiological measures. Timelier intervention, catalyzed by detection, will improve ROeI "return on earlier intervention" outcomes for clinical longevity and treatment costs.
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 Medical 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, smartwatches and smart devices provide the Consumer a number of sensory modalities to more conveniently, frequently, and affordably 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.
Digital biomarkers are device-generated physiological and behavioral data points generated from digital smart devices that can be used to detect or predict certain health diseases and outcomes.
LIFEdata can become a digital biomarker when a relationship is established between a health-related measure and a disease's prediction, detection, diagnosis, or response to a drug.
We are developing digital biomarkers by algorithmically training BioEngine4D via machine learning and artificial intelligence — premised around clinically-known pathophysiologies.
As our platform's Users generate 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. And digitally measure a diseases response to new drugs in clinical trials, leading to accelerated drug development.
Our solution to enhanced disease detection and drug response measures 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 and Medical professionals, intuitive to use, and value-additive to adopt.
Hundreds of engineers, years of R&D, and hundreds of millions of dollars were invested into the Apple Watch 4 ECG. This was a major validating tailwind for BioTrillion and the market we’ve been predicting for next-gen healthcare: digitally detecting developing diseases.
Solutions that will make us realize our health had been in the dark 99% of the time in retrospect.