Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.
Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warrant...
Saved in:
Main Authors: | Lathan Liou, Elizabeth Mostofsky, Laura Lehman, Soziema Salia, Francisco J Barrera, Ying Wei, Amal Cheema, Anuradha Lala, Andrew Beam, Murray A Mittleman |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2025-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0313600 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era
by: Lathan Liou, et al.
Published: (2025-01-01) -
Contemporary information social risks in the post-modern era
by: L. V. Tcerkasevich, et al.
Published: (2022-01-01) -
Malnutrition risk, weight loss, and subsequent survival in patients listed for heart transplantation
by: Tae Kyung Yoo, MD, MS, et al.
Published: (2025-02-01) -
Discourse of University: Knowledge in the Era of Contemporary Forms of Capitalism Development
by: E. I. Naumova, et al.
Published: (2021-04-01) -
Graft survival and mortality outcomes after kidney transplant in patients with lupus nephritis: a systematic review and meta-analysis
by: Weizhong Jiang, et al.
Published: (2024-12-01)