Identifying mortality risk factors amongst acute coronary syndrome patients admitted to Arabian Gulf hospitals using machine‐learning methods-Reference-Cited by-同舟云学术

Identifying mortality risk factors amongst acute coronary syndrome patients admitted to Arabian Gulf hospitals using machine‐learning methods

Published:2019-05-06 Issue:4 Volume:36 Page:
ISSN:0266-4720
Container-title:Expert Systems
language:en
Short-container-title:Expert Systems

Author:

Raza Syed Asif¹^ORCID,Thalib Lukman²,Al Suwaidi Jassim³,Sulaiman Kadhim⁴,Almahmeed Wael⁵,Amin Haitham⁶,AlHabib Khalid F.⁷

Affiliation:

1. Department of operations Management and Business StatisticsSultan Qaboos University Muscat Oman

2. Department of Public Health, College of Health SciencesQatar University Doha Qatar

3. Department of CardiologyHamad Medical Corporation Doha Qatar

4. Department of CardiologyRoyal Hospital Muscat Oman

5. Heart and Vascular InstituteCleveland Clinic Abu Dhabi Abu Dhabi United Arab Emirates

6. Mohammed Bin Khalifa Bin Sulman Al Khalifa Cardiac Centre Manama Bahrain

7. Department of Cardiac Sciences, King Fahad Cardiac Center, College of MedicineKing Saud University Riyadh Saudi Arabia

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12413

Reference48 articles.

1. Validation of the GRACE risk score for predicting death within 6 months of follow‐up in a contemporary cohort of patients with acute coronary syndrome;Abu‐Assi E.;Revista Española de Cardiología (English Edition),2010

2. Late Consequences of Acute Coronary Syndromes: Global Registry of Acute Coronary Events (GRACE) Follow-up

3. The TIMI risk score for unstable angina/non‐ST elevation MI: A method for prognostication and therapeutic decision making;Antman E. M.;Journal of the American Medical Association,2000

4. Objective Function Clustering

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