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January 26, 2021
A new risk score based on machine learning shows promise in predicting death, MI, and bleeding following an ACS event, potentially allowing clinicians to better individualize care.
The score, called PRAISE, is not the first score proposed to aide in outcome prediction and dual antiplatelet therapy (DAPT) duration decisions for this cohort of patients; one specifically designed for use in patients at high bleeding risk was published just last week. But the machine learning component is novel.
“The study showed that a machine learning approach is feasible and effective and more accurate than currently existing predictive scores that are based upon conventional and classical statistical methods,” study co-author Ovidio De Filippo, MD (University of Turin, Italy), told TCTMD. “We believe that this is an important step toward personalizing and having a tailored approach for patients suffering from acute coronary syndrome in order to predict their risk ....

France General , Fabrizio Dascenzo , Guglielmo Gallone , Christian Templin , Md University Of Turin , Md University Hospital Zurich , Ovidio De Filippo , Fabriziod Ascenzo , Davide Di Vece , University Hospital Zurich , பிரான்ஸ் ஜநரல் , கிறிஸ்துவர் டெம்ப்ளின் , ம்ட் பல்கலைக்கழகம் ஆஃப் டரின் , ம்ட் பல்கலைக்கழகம் மருத்துவமனை ஸுரி , பல்கலைக்கழகம் மருத்துவமனை ஸுரி ,