Summary
Researchers from the Perelman School of Medicine at the University of Pennsylvania and the University of Florida College of Medicine are creating a set of artificial intelligence algorithms to determine patient risk for various rare diseases. To develop the AI method, researchers will use data from Patient-Centered Clinical Research Networks which provides information on over 27 million patients. In September, researchers at Mayo Clinic created an AI-based risk prediction model that used labor characteristics to define potential childbirth outcomes.
Show Notes
Researchers from the Perelman School of Medicine at the University of Pennsylvania and the University of Florida College of Medicine are creating a set of artificial intelligence (AI) algorithms to determine patient risk for various rare diseases. After receiving a $4.7 million grant from the National Institutes of Health, researchers are working to apply AI and machine learning to information from patient medical records to predict the risk of rare disease development. The set of AI algorithms, known as the Predictive Analytics via Networked Distributed Algorithms for multi-system diseases (PANDA) system, will scan data from patient EHRs to enable earlier diagnosis. EHR data such as lab test results, comorbid conditions, and former treatments will be used to create the algorithms. For example, in September, researchers at Mayo Clinic created an AI-based risk prediction model that used labor characteristics to define potential childbirth outcomes.
