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Computational biomarkers can identify at-risk heart attack victims

Subtle markers of heart damage hidden in plain sight among hours of EKG recordings could help doctors identify which heart attack patients are at high risk of dying soon, researchers from the University of Michigan, MIT, Harvard Medical School, and Brigham and Women s Hospital in Boston have discovered, The findings could help match tens of thousands of cardiac patients with life-saving treatment in time. Missing 70 percent of high-risk patients Today s methods for determining which heart attack victims need the most aggressive treatments can identify some groups of patients at a high risk of complications. Using data mining and machine learning techniques, the researchers sifted through 24-hour continuous electrocardiograms (EKGs or ECGs) from 4,557 heart attack patients. These could be prevented with medication or implantable defibrillators, which can shock the heart back into rhythm.

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September 29, 2011 October 02, 2011 Kurzweilai Applications MachineLearning NewsFinder Text English HTML

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  1. (sim=0.650) 3432: Datamining Could Predict Heart Attack Risk
  2. (sim=0.521) 3476: Computer Science Gives a Boost to Heart Health

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Page last modified on October 02, 2011, at 06:12 AM