A proposed machine-learning method applied to filtered ECGs from portable devices can identify cardiac health risks and estimate their severity.
May support portable ECG risk stratification; hypothesis-generating and requires prospective validation before clinical use.
Portable medical devices generate volumes of data that could be useful in identifying health risks. The proposed method filters patients' electrocardiograms (ECGs) and applies machine-learning classifiers to identify cardiac health risks and estimate severity. The authors present the results of applying their method in a case study.
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Hijazi et al. (2016) studied this question.
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