Machine learning informs a new tool to guide treatment for acute decompensated heart failure

A recent study co-authored by Dr. Matthew Segar, a third-year cardiovascular disease fellow at The Texas Heart Institute and led by his research and residency mentor, University of Texas Southwestern Medical Center’s Dr. Ambarish Pandey, utilized a machine learning-based approach to identify, understand, and predict diuretic responsiveness in patients with acute decompensated heart failure (ADHF).

This article was originally published on MedicalXpress.com

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