Machine learning-based marker for coronary artery disease: d

Electronic health record-based machine learning was used to generate an in-silico
marker for coronary artery disease that can non-invasively quantify atherosclerosis
and risk of death on a continuous spectrum, and identify underdiagnosed individuals.

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Plomin , Istarska , Croatia , United States , Denmark , Chiswell , Dorset , United Kingdom , Croat , American , Danish , Nat Genet , Coll Cardiol , Schjerning Olsen , Goff Jr , Smith Jr , Cardiovasc Genet , J Cardiovasc Pharmacother , Preventive Cardiovascular Nurses Association , American College Of Cardiology Foundation , American Heart Association , Society Of Thoracic Surgeons , American Association For Thoracic Surgery , Society For Cardiovascular Angiography , American College , Rev Cardiol , Intern Med , Transl Med , American Heart Association Task Force , Practice Guidelines , American Association , Thoracic Surgery , Cardiovascular Angiography , Englj Med , Rev Genet , United Kingdom Biobank , Thorac Surg , Stat Softw , Res Methodol , Diabetes Endocrinol ,