The unique feature offered is a Bayesian-logic architecture that calculates weighted averages for all determinants that are data-based, with regard to their association with suspected or confirmed coronavirus. The result is the ability to actually calculate a probability that a specific disease is present, absent or changing in presentation.
A critical number of cases need to accrue from which weighted averages may be derived with statistical validity. Prior to that point, they may be estimated from review of existing literature or by clinical inference by experts in infectious disease and placed into the e-infrastructure.
Once the critical number of cases has been reached, the productive tool is self-functionalized in accordance with rigorous statistical theory and probabilities of unknown cases may be calculated. As more cases accrue, the machine-learning tool incorporates these cases into the existing weighted averages automatically, which translates into better predictability.
This process has infinite scalability, and the process has been granted patent status for its novelty.
In this way, the system provides diagnostic and triage support, as well as targeted data mining for clinical trials by providers and pharmas.
Next