Why the study?
Identifying idiosyncratic predictors of aortic stenosis disease progression could better delineate a recommended period for subsequent evaluation and impact hard outcomes.
Can machine learning identify clinical and echocardiographic predictors of disease progression and clinical outcomes in patients with moderate aortic stenosis?
Can machine learning identify clinical and echocardiographic predictors of disease progression and clinical outcomes in patients with moderate aortic stenosis?
Machine learning may help identify clinical and echocardiographic predictors of disease progression in moderate aortic stenosis.
Identifying idiosyncratic predictors of aortic stenosis (AS) disease progression could better delineate a recommended period for subsequent evaluation and impact hard outcomes. Machine learning could elucidate these predictors and calculate the probability of an adverse event or intervention,
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Gada et al. (2020) studied this question.
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