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Abstract #5666

Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis

Bharath Ambale Venkatesh1, Xiaoying Yang2, Colin Wu3, W. Gregory Hundley4, Antoinette S Gomes5, Eliseo Guallar6, David A Bluemke3, and Joao A C Lima6

1Radiology, Johns Hopkins University, Baltimore, MD, United States, 2George Washington University, Washington DC, DC, United States, 3National Institutes of Health, Bethesda, MD, United States, 4Wake Forest University Health Sciences, Winston-Salem, NC, United States, 5UCLA School of Medicine, Los Angeles, CA, United States, 6Johns Hopkins University, Baltimore, MD, United States

Event prediction has been the cornerstone of cardiovascular epidemiology and have allowed us to characterize sub-clinical disease processes and target key risk factors for modification. Epidemiological studies used to derive such predictive models frequently contain hundreds of variables from multiple tests. Random survival forests may be an effective machine learning strategy for incident event prediction in large populations with large phenotypic datasets. These methods do not require a priori assumptions regarding causality and may thus be suitable to defining the role of novel biomarkers and tests (such as imaging, biomarker panels, ECG, etc) for cardiovascular disease prediction. We explore the role of MRI in the prediction of incident heart failure and all-cause death.

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