Machine Learning Informs a New Tool to Guide Treatment for Acute Decompensated Heart Failure
New phenomapping tool and clinical score could lead to personalized strategies for patients hospitalized with ADHF
Source: dicardiology.com
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Machine learning could personalize diuretic treatment for patients with 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).
Source: news-medical.net