  This paper achieves state of the art results for the ICD code prediction task
using the MIMIC-III dataset. This was achieved through the use of Clinical BERT
(Alsentzer et al., 2019). embeddings and text augmentation and label balancing
to improve F1 scores for both ICD Chapter as well as ICD disease codes. We
attribute the improved performance mainly to the use of novel text augmentation
to shuffle the order of sentences during training. In comparison to the Top-32
ICD code prediction (Keyang Xu, et. al.) with an F1 score of 0.76, we achieve a
final F1 score of 0.75 but on a total of the top 50 ICD codes.
