首页|University Health Network Reports Findings in Cardiomyopathies (Machine Learning Identifies Arrhythmogenic Features of QRS Fragmentation in Human Cardiomyopathy : Implications for Improving Risk Stratification)
University Health Network Reports Findings in Cardiomyopathies (Machine Learning Identifies Arrhythmogenic Features of QRS Fragmentation in Human Cardiomyopathy : Implications for Improving Risk Stratification)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Heart Disorders and Di seases - Cardiomyopathies is the subjectof a report. According to news reportin g originating from Toronto, Canada, by NewsRx correspondents,research stated, “ Heterogeneous ventricular activation can provide the substrate for ventricular a rrhythmias(VA), but its manifestation on the electrocardiogram (ECG) as a risk stratifier is not well-defined. Tocharacterize the spatiotemporal features of Q RS peaks that best predict VA in patients with cardiomyopathy(CM) using machine learning (ML).”
TorontoCanadaNorth and Central Ameri caCardiologyCardiomyopathiesCardiovascular Diseases and ConditionsCyborg sEmerging TechnologiesHealth and MedicineHeart DiseaseHeart Disorders an d DiseasesMachine Learning
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