  Automatic ICD coding is the task of assigning codes from the International
Classification of Diseases (ICD) to medical notes. These codes describe the
state of the patient and have multiple applications, e.g., computer-assisted
diagnosis or epidemiological studies. ICD coding is a challenging task due to
the complexity and length of medical notes. Unlike the general trend in
language processing, no transformer model has been reported to reach high
performance on this task. Here, we investigate in detail ICD coding using
PubMedBERT, a state-of-the-art transformer model for biomedical language
understanding. We find that the difficulty of fine-tuning the model on long
pieces of text is the main limitation for BERT-based models on ICD coding. We
run extensive experiments and show that despite the gap with current
state-of-the-art, pretrained transformers can reach competitive performance
using relatively small portions of text. We point at better methods to
aggregate information from long texts as the main need for improving BERT-based
ICD coding.
