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ICD coding from electronic clinical records is a manual, time-consuming andexpensive process. Code assignment is, however, an important task for billingpurposes and database organization. While many works have studied the problemof automated ICD coding from free text using machine learning techniques, mostuse records in the English language, especially from the MIMIC-III publicdataset. This work presents results for a dataset with Brazilian Portugueseclinical notes. We develop and optimize a Logistic Regression model, aConvolutional Neural Network (CNN), a Gated Recurrent Unit Neural Network and aCNN with Attention (CNN-Att) for prediction of diagnosis ICD codes. We alsoreport our results for the MIMIC-III dataset, which outperform previous workamong models of the same families, as well as the state of the art. Compared toMIMIC-III, the Brazilian Portuguese dataset contains far fewer words perdocument, when only discharge summaries are used. We experiment concatenatingadditional documents available in this dataset, achieving a great boost inperformance. The CNN-Att model achieves the best results on both datasets, withmicro-averaged F1 score of 0.537 on MIMIC-III and 0.485 on our dataset withadditional documents.

Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes