Predicting extremely low body weight from 12-lead electrocardiograms using a deep neural network
Ken Kurisu,
Tadahiro Yamazaki,
Kazuhiro Yoshiuchi
Abstract:Previous studies have successfully predicted overweight status by applying deep learning to 12-lead electrocardiogram (ECG); however, models for predicting underweight status remain unexplored. Here, we assessed the feasibility of deep learning in predicting extremely low body weight using 12-lead ECGs, thereby investigating the prediction rationale for highlighting the parts of ECGs that are associated with extremely low body weight. Using records of inpatients predominantly with anorexia nervosa, we trained … Show more
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