Abstract
Primary percutaneous coronary intervention (PPCI) is a minimally invasive procedure to unblock the arteries which carry blood to the heart. This procedure is carried out once patients are accepted based on the STEMI criteria upon the assessment of 12-lead ECG. This paper reports the analyses of a dataset compiled from patients accepted for PPCI. The primary objective was to explore the features which may predict 30days mortality. The 30 day mortality was?? The main features identified were a patient's age, sex, door to balloon time, call time, pain time, and activation status. Together these features appear to be a predictor of 30day mortality in patients referred for PPCI (76% accuracy, 70% sensitivity and 85% specificity).
Original language | English |
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Title of host publication | Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
Editors | Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu |
Place of Publication | San Diego, CA, USA |
Publisher | IEEE Xplore |
Pages | 1315-1317 |
Number of pages | 3 |
ISBN (Electronic) | 978-1-7281-1867-3 |
ISBN (Print) | 978-1-7281-1868-0 |
DOIs | |
Publication status | Published (in print/issue) - 6 Feb 2020 |
Event | 2019 IEEE International Conference on Bioinformatics and Biomedicine - Duration: 18 Nov 2019 → 21 Nov 2019 https://ieeebibm.org/BIBM2019/ |
Conference
Conference | 2019 IEEE International Conference on Bioinformatics and Biomedicine |
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Abbreviated title | BIBM |
Period | 18/11/19 → 21/11/19 |
Internet address |
Keywords
- Machine learning
- STEMI
- acute myocardial infarction
- mortality
- heart attacks
- health data analytics