  Clinical notes are unstructured text generated by clinicians during patient
encounters. Clinical notes are usually accompanied by a set of metadata codes
from the International Classification of Diseases(ICD). ICD code is an
important code used in various operations, including insurance, reimbursement,
medical diagnosis, etc. Therefore, it is important to classify ICD codes
quickly and accurately. However, annotating these codes is costly and
time-consuming. So we propose a model based on bidirectional encoder
representations from transformers (BERT) using the sequence attention method
for automatic ICD code assignment. We evaluate our approach on the medical
information mart for intensive care III (MIMIC-III) benchmark dataset. Our
model achieved performance of macro-averaged F1: 0.62898 and micro-averaged F1:
0.68555 and is performing better than a performance of the state-of-the-art
model using the MIMIC-III dataset. The contribution of this study proposes a
method of using BERT that can be applied to documents and a sequence attention
method that can capture important sequence in-formation appearing in documents.
