In the study, which was published in JACC: Cardiovascular Interventions, Mayo Clinic researchers applied the algorithm to a retrospective analysis of data from the Rochester, Minn.-based medical center’s PCI registry. The information included EHR, demographic and social data from nearly 12,000 Mayo Clinic patients, who had collectively undergone more than 14,000 PCIs.
As a result, compared to standard regression methods, the algorithm was proven to be a better predictor of mortality 180 days post-PCI and of 30-day rehospitalization for congestive heart failure. Additionally, the algorithm successfully identified patient subgroups at an elevated risk of other post-PCI complications and readmission.
Medial EarlySign has developed several other machine learning-powered solutions. Most recently, the company partnered with Danville, Pa.-based Geisinger to develop and deploy a suite of new solutions to assess patients’ risk of contracting various high-burden diseases.
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