  ICD coding from electronic clinical records is a manual, time-consuming and
expensive process. Code assignment is, however, an important task for billing
purposes and database organization. While many works have studied the problem
of automated ICD coding from free text using machine learning techniques, most
use records in the English language, especially from the MIMIC-III public
dataset. This work presents results for a dataset with Brazilian Portuguese
clinical notes. We develop and optimize a Logistic Regression model, a
Convolutional Neural Network (CNN), a Gated Recurrent Unit Neural Network and a
CNN with Attention (CNN-Att) for prediction of diagnosis ICD codes. We also
report our results for the MIMIC-III dataset, which outperform previous work
among models of the same families, as well as the state of the art. Compared to
MIMIC-III, the Brazilian Portuguese dataset contains far fewer words per
document, when only discharge summaries are used. We experiment concatenating
additional documents available in this dataset, achieving a great boost in
performance. The CNN-Att model achieves the best results on both datasets, with
micro-averaged F1 score of 0.537 on MIMIC-III and 0.485 on our dataset with
additional documents.
