June 1, 2016Open Access
Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis
Structured PICO
Does machine learning combining clinical and CCTA data improve prediction of 5-year all-cause mortality in patients with suspected coronary artery disease compared to existing clinical or CCTA metrics alone?
P
Population
Patients with suspected coronary artery disease
I
Intervention
Machine learning combining clinical and coronary computed tomography angiography (CCTA) data
C
Comparator
Existing clinical or CCTA metrics alone
O
Outcome
5-year all-cause mortality (ACM)hard clinical
Machine learning models integrating clinical and CCTA data improve 5-year mortality prediction over traditional metrics in patients with suspected CAD.
Abstract
Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone.
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Authors
M
Manish Motwani
Manchester Royal Infirmary
D
Damini Dey
Cardiac Imaging
Daniel S. Berman
Marquette University
Journals
European Heart Journal
Actions
Institutions
Cornell University
University of British Columbia
Emory University
References and Citations
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