Predicting 30-day mortality after ST elevation myocardial infarction: Machine learning- based random forest and its external validation using two independent nationwide datasets

Hadanny, A, Shouval, R, Wu, J orcid.org/0000-0001-6093-599X et al. (8 more authors) (2021) Predicting 30-day mortality after ST elevation myocardial infarction: Machine learning- based random forest and its external validation using two independent nationwide datasets. Journal of Cardiology, 78 (5). pp. 439-446. ISSN 0914-5087

Abstract

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Item Type: Article
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Crown Copyright © 2021 Published by Elsevier Ltd on behalf of Japanese College of Cardiology. This is an author produced version of an article published in Journal of Cardiology (JC). Uploaded in accordance with the publisher's self-archiving policy.

Keywords: ST-segment elevation myocardial infarction; Machine learning; Data mining; Outcome; Mortality
Dates:
  • Published: November 2021
  • Published (online): 19 June 2021
  • Accepted: 26 May 2021
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Dentistry (Leeds) > Applied Health and Clinical Translation (Leeds)
The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Clinical & Population Science Dept (Leeds)
Funding Information:
Funder
Grant number
British Heart Foundation
PG/19/54/34511
Depositing User: Symplectic Publications
Date Deposited: 28 May 2021 12:10
Last Modified: 21 Nov 2024 15:21
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.jjcc.2021.06.002
Open Archives Initiative ID (OAI ID):

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