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.
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.
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international classification of diseases
International Classification of Diseases
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a variety of
various
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transformer
transformers
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ap-proach on the
approach on the medical information mart for intensive care III (
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)
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Macro-aver-aged
macro-averaged
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Micro-averaged
micro-averaged
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,
D
previous
A
using the MIMIC-III dataset
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im-portant sequence information
important sequence in-formation