State-of-the-art machine learning improves predictive accuracy of 1-year survival after heart transplantation
ESC Heart Fail
.
2021 Aug;8(4):3433-3436.
doi: 10.1002/ehf2.13425.
Epub 2021 May 18.
Authors
Polydoros N Kampaktsis
1
,
Serafeim Moustakidis
2
,
Aspasia Tzani
3
,
Ilias P Doulamis
4
,
Anastasios Drosou
5
,
Andreas Tzoumas
6
,
Rabea Asleh
7
,
Alexandros Briasoulis
8
Affiliations
1
Division of Cardiology, New York University Langone Medical Center, New York City, NY, USA.
2
AiDEAS, Tallinn, Estonia.
3
Brigham and Women's Hospital Heart and Vascular Center, Harvard Medical School, Boston, MA, USA.
4
Division of Cardiac Surgery, Boston's Children Hospital, Boston, MA, USA.
5
Information Technologies Institute, National Center for Research and Technology, Thessaloniki, Greece.
6
Aristotle University of Thessaloniki Medical School, Thessaloniki, Greece.
7
Hadassah Medical Center, Jerusalem, Israel.
8
Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, USA.
PMID:
34008301
PMCID:
PMC8318480
DOI:
10.1002/ehf2.13425
No abstract available
Publication types
Letter
MeSH terms
Algorithms
Heart Transplantation*
Humans
Machine Learning*