Med7: a transferable clinical natural language processing model for electronic health records
Figure 1: Model pre-training characteristics for various combinations of convolutional layers dimensions. Time and the resulting model size are reported in minutes and megabytes (MB) respectively.
['Configuration', 'Width', 'Depth', 'Time', 'Size']
The pre-training task was performed on the entire MIMIC-III data set for 350 epochs using a number of configurations of the width and depth of the convolutional layers. Each configuration was trained on a single GTX 2080 Ti GPU. The corresponding training losses, logarithmically scaled, are plotted in Fig.