Flahy uses knowledge graphs to support AI-powered healthcare

Flahy uses knowledge graphs to support AI-powered healthcare

AI is helping companies interpret biological data for drug discovery and more personalized care.

Healthcare technology company Flahy Inc. is applying that approach to clinical decision support, bringing together information that could affect a person’s next step in prevention or treatment, according to Jagjit Singh (pictured, right), founder and chief executive officer of Flahy.

“There’s so much knowledge that we have to process,” he said “The knowledge layer is very critical because it connects your data layer to your model layer and it tells the model exactly what facts matter and what a specific data point implies. We process a big context of biological and clinical information that’s spread across a big graph. And that decides … for a given person what decision tree or what route to take from here in terms of better treatment decision-making.”

Singh spoke with host of theCUBE, John Furrier (left), for theCUBE + NYSE Wired: AI Luminaries interview series on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how knowledge graphs can connect patient information to support personalized healthcare decisions. (* Disclosure below.)

Using knowledge graphs in AI-powered healthcare

Flahy’s team has spent years building a graph-based information database and training its model to recognize relationships among data points, according to Singh. He illustrated the approach with a hypothetical patient whose genetic mutation and cholesterol marker could affect how a new clinical finding is interpreted.

“I’m looking for this one person [who] had a genetic mutation and this specific high cholesterol marker. If I have a new clinical signal, would that change how this person should be treated? That is a very different question. It’s a graphical question by design.”

Flahy works with graph technology companies, including Neo4j Inc., according to Singh. One challenge is incorporating wearable-device readings into a graph alongside other health information so the system can interpret changes over time.

“We have created our own proprietary engines,” he said. “If you look at clinical decision-making or the problem that I’m trying to solve, it is always a traversal problem. When it comes to longitudinal data, you have to find a way to put it in a specific graph. For me, that’s been a challenge which we are effectively trying to solve in terms of how to connect the dots across separate modalities.”

AI-powered healthcare requires a clear explanation of how information informs a decision, according to Singh. Flahy’s consumer offering, FlahyLife, combines biological and health information to guide next steps in prevention, early detection and treatment selection, according to the company.

“We are working with leading clinical laboratories and health systems and trying to deploy our platform in terms of better clinical decision making and closing care gaps,” Singh said. “If you could simply connect the graph, you can reason in a correct way. You’re serving the right people for the right test at the right time.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of theCUBE + NYSE Wired: AI Luminaries interview series:

(* Disclosure: TheCUBE is a paid media partner for theCUBE + NYSE Wired: AI Luminaries interview series. Neither Neo4j, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE