Researchers at UT Southwestern Medical Center have developed a machine learning model that can identify patients with diabetic cardiomyopathy, a heart condition characterized by abnormal changes in the heart’s structure and function that predisposes them to increased risk of heart failure. The findings, published in the European Journal of Heart Failure, offer a data-driven method to detect a high-risk diabetic cardiomyopathy phenotype, enabling early interventions that could help prevent heart failure in this vulnerable population.
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