Machine Learning Identification of Modifiable Predictors of Patient Outcomes After Transcatheter Aortic Valve Replacement

Machine Learning Identification of Modifiable Predictors of Patient Outcomes After Transcatheter Aortic Valve Replacement

Background: Transcatheter aortic valve replacement (TAVR) is an important treatment option for patients with severe symptomatic aortic stenosis. It is important to identify predictors of excellent outcomes (good clinical outcomes, more time spent at home) after TAVR that are potentially amenable to...

Full description

Bibliographic Details
Main Authors: Mark J. Russo, MD, Sammy Elmariah, MD, MPH, Tsuyoshi Kaneko, MD, David V. Daniels, MD, Rajendra R. Makkar, MD, Soumya G. Chikermane, PhD, Christin Thompson, PhD, Jose Benuzillo, MS, Seth Clancy, MPH, Amber Pawlikowski, MSN, Skye Lawrence, BA, Jeff Luck, MBA, PhD
Format: Article
Language:English
Published: Elsevier 2024-08-01
Series:JACC: Advances
Subjects:
artificial intelligence
cardiology
care process
patient-centered
random forest
TAVR
Online Access:http://www.sciencedirect.com/science/article/pii/S2772963X24003119

Internet

http://www.sciencedirect.com/science/article/pii/S2772963X24003119

Similar Items