Mayo Clinic study finds AI can predict post-PCI hospitalization, death

A machine learning algorithm from Tel Aviv-based Medial EarlySign effectively predicted patients' risk of complications and readmission after undergoing percutaneous coronary intervention, according to recent study.

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.

More articles on AI:
'AI hesitancy' is hindering the potential of medical chatbots, study finds
Mayo Clinic researchers develop AI to measure physiological health from ECG
Physician viewpoint: AI needs a 'Turing test' to determine medical feasibility

Copyright © 2022 Becker's Healthcare. All Rights Reserved. Privacy Policy. Cookie Policy. Linking and Reprinting Policy.


Featured Whitepapers

Featured Webinars