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

• URL: http://arxiv.org/abs/2008.01515v1
• Date: Wed, 29 Jul 2020 22:12:26 GMT
• Title: Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes
• Authors: Arthur D. Reys, Danilo Silva, Daniel Severo, Saulo Pedro, Marcia M. de Souza e S\'a, Guilherme A. C. Salgado
• Abstract summary: We develop and optimize a Logistic Regression model, a Convolutional Neural Network (CNN), a Gated Recurrent Unit Neural Network and a CNN with Attention for prediction of diagnosis ICD codes. Compared to MIMIC-III, the Brazilian Portuguese dataset contains far fewer words per document, when only discharge summaries are used. 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.
• Score: 4.971638713979981
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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