QuantHealth's proprietary knowledge-graph, based on 30+ sources and curated content, has over 5M datapoints, mapping the full biological cascade from therapeutic entity, to target, pathway, cell, anatomy, and disease.
When simulating novel drugs, our model represents the drug based on hundreds or thousands of connections within the knowledge graph to derive a unique signature for your drug based on its target, structure, and clinical context.
With an enormous database of 350M lives, and over 10,000 data points per patient spanning over 10 years of history coming from both structured and unstructured data, we can model the most nuanced sub-populations, and capture a patient's entire journey from diagnosis to outcome.
By decomposing any patient journey, and digitally recomposing it with any treatment (either novel or approved) at a specific point in the patient's journey, we have built a digital-drug and digital-twin in-one platform, that can simulate any clinical scenario with astounding flexibility and clinical depth.
The outcome prediction model measures the virtual clinical result of that specific patient with that specific treatment
With a single-patient-outcome resolution, we can go ahead and virtually test any group of patients that answer specific inclusion/exclusion criteria, and measure their results