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Tuesday, April 21, 2026
Traditional clinical models rely on a limited number of known risk factors, such as reduced heart function or prior heart attacks
In the context of sudden cardiac arrest, researchers are using these tools to analyze a wide range of information
Machine learning models can incorporate hundreds or thousands of variables at once, learning subtle combinations of signals that may indicate elevated risk
Sudden cardiac arrest (SCA) strikes without warning, often in people who show few or no prior symptoms
Electrocardiograms (ECGs), medical histories, genetic data, imaging results, and even data from wearable devices: machine learning analyzes and finds a pattern

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