Two things: We couple large language modeling with a proprietary hypergraph that represents the complexity of precision medicine. This approach gets the models closer than any other technology to physician level understanding of the data.
Second, we solved computational problems to traverse Hypergraphs at laser fast speed and affordable cost enabling the technology to work in real-life scenarios.
What is a Hypergraph vs. a Knowledge graph?
A hypergraph is a generalization of a graph, allowing edges to connect any number of vertices. Knowledge graphs are typically based on a simple graph, limiting the representation of complex relationships.
Why can’t I build my own Hypergraph?
Building a clinical Hypergraph requires extensive domain expertise, large-scale data curation, and complex algorithmic development. It is a time-consuming and resource-intensive process that most organizations may find challenging to undertake independently.
Hypergraphs are slow?
Not necessarily. Our Hypergraph technology utilizes innovative algorithms and data structures specifically designed for efficient hypergraph analysis, enabling quick traversal and querying even with a large number of nodes and relationships.
Who are the experts behind the Hypergraph?
Our Hypergraph is developed by a team of AI scientists and clinical experts who have worked together for years to curate knowledge and build a scalable solution tailored to the unique challenges of the clinical domain.
What ontology are you using?
We have developed a proprietary ontology called the “Generative Ontology” that combines principles of generative grammar and ontology to create a scalable and adaptable system for modeling the clinical domain.
Can I trust the logic?
Yes, our Hypergraph is expert-curated and open, allowing clinical teams to audit the logic. The underlying language models are trained on actual medical data and records, not just internet data.
Is this a rule-based system?
No, our platform combines both symbolic reasoning (through the Hypergraph) and machine learning (through language models) to achieve a more comprehensive and flexible approach to clinical reasoning, going beyond traditional rule-based systems.
