The era of big data in medicine
1
is undoubtedly upon us, and the volume of data available to clinicians and researchers
for predictive analytics will continue to expand as we increasingly incorporate information
from electronic health records, wearable biosensors, genomics, proteomics, and other
metabolomics data in the future.
2
With this explosion in data, there is increasing interest in—and enthusiasm for—the
potential of artificial intelligence (particularly predictive analytics based on machine
learning [ML] approaches) to usher in an era of data-driven precision medicine. In
fact, the Food and Drug Administration has already approved a number of ML products
for use in cardiology (such as the AliveCor Heart Monitor [AliveCor Inc, Mountain
View, California] for detecting atrial fibrillation) and ML-derived polygenic risk
scores have been reported for several cardiac conditions.
3
However, the systematic review by Cho et al.,
4
comparing the performance of predictive models generated by ML methods with those
arising from conventional statistical methods in this issue of the Canadian Journal of Cardiology is a sobering reminder that, as Chen and Asch warned, “machine learning now rides
atop the peak of inflated expectations.”
5
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Article info
Publication history
Published online: March 09, 2021
Accepted:
March 2,
2021
Received:
February 16,
2021
Footnotes
See article by Cho et al., pages 1207–1214 of this issue.
See page 1158 for disclosure information.
Identification
Copyright
© 2021 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.
ScienceDirect
Access this article on ScienceDirectLinked Article
- Machine Learning Compared With Conventional Statistical Models for Predicting Myocardial Infarction Readmission and Mortality: A Systematic ReviewCanadian Journal of CardiologyVol. 37Issue 8
- PreviewMachine learning (ML) methods are increasingly used in addition to conventional statistical modelling (CSM) for predicting readmission and mortality in patients with myocardial infarction (MI). However, the two approaches have not been systematically compared across studies of prognosis in patients with MI.
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