Key result
Machine learning algorithms using clinical variables predict stable obstructive CAD.
Why the study?
Machine learning may analyze clinical information for more accurate predictions, and researchers sought to develop a model using clinical variables to predict stable obstructive coronary artery disease.
Does a machine learning model utilizing clinical variables predict the presence of stable obstructive coronary artery disease?
Does a machine learning model utilizing clinical variables predict the presence of stable obstructive coronary artery disease?
Answers are grounded in this paper and cite their sources.
ML model may refine stable CAD prediction; hypothesis-generating and requires prospective validation before adoption.
Machine learning (ML) might be useful to analysis various clinical information to make more accurate predictions. We sought to develop a ML model, utilizing clinical variables to predict the presence of stable obstructive coronary artery disease (CAD). We retrospectively analyzed 4906 patients who
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Kim et al. (2020) studied this question. Machine learning algorithms utilizing clinical variables were developed to predict the presence of stable obstructive coronary artery disease in a retrospective cohort of 4906 patients.
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