Medical Code Prediction from Discharge Summary: Document to Sequence BERT using Sequence Attention

• URL: http://arxiv.org/abs/2106.07932v1
• Date: Tue, 15 Jun 2021 07:35:50 GMT
• Title: Medical Code Prediction from Discharge Summary: Document to Sequence BERT using Sequence Attention
• Authors: Tak-Sung Heo, Yongmin Yoo, Yeongjoon Park, Byeong-Cheol Jo
• Abstract summary: We propose a model based on bidirectional encoder representations from transformer (BERT) using the sequence attention method for automatic ICD code assignment. We evaluate our ap-proach on the MIMIC-III benchmark dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 a variety of 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 transformer (BERT) using the sequence attention method for automatic ICD code assignment. We evaluate our ap-proach on the MIMIC-III benchmark dataset. Our model achieved performance of Macro-aver-aged F1: 0.62898 and Micro-averaged F1: 0.68555, and is performing better than a performance of the previous state-of-the-art model. 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 im-portant sequence information appearing in documents.

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