Study Shows Machine Learning’s Potential to Predict Cancer Therapy-Related Cardiac Risk

From Cleveland Clinic Lerner Research Institute: Machine learning-based approaches to risk assessment can be highly effective in predicting various types of cardiac dysfunction among cancer survivors who have received cardiotoxic cancer therapies, according to a new retrospective longitudinal study by researchers from Cleveland Clinic’s Lerner Research Institute; Heart, Vascular & Thoracic Institute; and Taussig Cancer Institute.

Portrait of Feixiong Cheng wearing blue dress shirt, dark suit jacket and tie.

Published in the Journal of the American Heart Association, the study represents the first reported large-scale use of a machine learning-based approach for evaluating complications from cancer therapies that can contribute to cardiovascular disease. The research team, led by Feixiong Cheng, PhD, assistant staff in the Genomic Medicine Institute, and Patrick Collier, MD, PhD, co-director of Cleveland Clinic’s Cardio-Oncology Center, developed and evaluated risk assessment machine learning models for six forms of CTRCD: heart failure, atrial fibrillation, coronary artery disease, myocardial infarction, stroke and de novo CTRCD (CTRCD developed after cancer therapy).

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