The approach is based on the notion that greater variability between heartbeats reflects greater risk. Scientists trained the machine learning system using historical data for patient outcomes. If a patient survived, their heartbeats were deemed relatively normal; if a patient died, their heart activity was considered risky. The ultimate risk score comes by averaging the prediction from each set of consecutive heartbeats. There's plenty of work to be done, including refining the training data to account for more ages, ethnic backgrounds and genders.
Sep-16-2019, 04:57:10 GMT