Researchers from the Perelman School of Medicine at the University of Pennsylvania and the University of Florida College of Medicine are working to create AI algorithms that can predict whether patients will develop rare diseases.
Over the next four years, the researchers will use de-identified data from the Patient-Centered Clinical Research Networks to develop sets of AI algorithms that can help identify which patients are at risk of five different types of vasculitis and two different types of spondyloarthritis, according to a Sept. 21 press release.
This prediction model, dubbed Predictive Analytics via Networked Distributed Algorithms for multisystem diseases, will use information from patients' EHRs to increase the chances of patients being diagnosed sooner.
Once the algorithms are built, the researchers will test their predictive power across more than 10 health systems.
"The increasing availability of real-world data, such as electronic health records collected through routine care, provides a golden opportunity to generate real-world evidence to inform clinical decision-making," said Jiang Bian, PhD, chief data scientist at the University of Florida Health System and principal investigator of the study. "Nevertheless, to leverage these large collections of real-world data, which are often distributed across multiple sites, novel distributed algorithms like PANDA are much needed."
The study is funded by a $4.7 million grant from the National Institutes of Health.