  Predicting future clinical events helps physicians guide appropriate
intervention. Machine learning has tremendous promise to assist physicians with
predictions based on the discovery of complex patterns from historical data,
such as large, longitudinal electronic health records (EHR). This study is a
first attempt to demonstrate such capabilities using raw echocardiographic
videos of the heart. We show that a large dataset of 723,754
clinically-acquired echocardiographic videos (~45 million images) linked to
longitudinal follow-up data in 27,028 patients can be used to train a deep
neural network to predict 1-year mortality with good accuracy (area under the
curve (AUC) in an independent test set = 0.839). Prediction accuracy was
further improved by adding EHR data (AUC = 0.858). Finally, we demonstrate that
the trained neural network was more accurate in mortality prediction than two
expert cardiologists. These results highlight the potential of neural networks
to add new power to clinical predictions.
