Interpretable machine learning for in-hospital mortality risk prediction in patients with ST-elevation myocardial infarction after percutaneous coronary interventions-Reference-Cited by-同舟云学术

Interpretable machine learning for in-hospital mortality risk prediction in patients with ST-elevation myocardial infarction after percutaneous coronary interventions

Published:2024-03 Issue: Volume:170 Page:107953
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Shakhgeldyan Karina Iosephovna^ORCID,Kuksin Nikita Sergeevich^ORCID,Domzhalov Igor Gennadievich,Rublev Vladislav Yurievich^ORCID,Geltser Boris Izrajlevich^ORCID

Funder

Far Eastern Federal University

Ministry of Education and Science of the Russian Federation

Publisher

Elsevier BV

Reference38 articles.

1. Mortality and Causes of Death Collaborators. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015;Lancet,2015

2. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation

3. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation

4. Global Registry of Acute Coronary Events (GRACE) hospital discharge risk score accurately predicts long-term mortality post acute coronary syndrome;Tang;Am. Heart J.,2007

5. Predictors of hospital mortality in the global registry of acute coronary events;Granger;Arch. Intern. Med.,2003

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Using ensemble learning and hierarchical strategy to predict the outcomes of ESWL for upper ureteral stone treatment;Computers in Biology and Medicine;2024-09