Improving Automatic KCD Coding: Introducing the K o DAK and an Optimized Tokenization Method for K orean Clinical Documents (2023)

Abstract: International Classification of Diseases (ICD) coding is the task of assigning a patient’s electronic health records into standardized codes, which is crucial for enhancing medical services and reducing healthcare costs. In Korea, automatic Korean Standard Classification of Diseases (KCD) coding has been hindered by limited resources, differences in ICD systems, and language-specific characteristics. Therefore, we construct the Korean Dataset for Automatic KCD coding (KoDAK) by collecting and preprocessing Korean clinical documents. In addition, we propose a tokenization method optimized for Korean clinical documents. Our experiments show that our proposed method outperforms Korean Medical BERT (KM-BERT) in Macro-F1 performance by 0.14%p while using fewer model parameters, demonstrating its effectiveness in Korean clinical documents.

## introduction international classification of diseases (icd) coding is the assigning of standardized codes from the icd system to patients' electronic health records. this is essential for standardizing information across medical institutions, and it serves as the foundation for the analysis of medical statistics. traditionally, professional coders performed this task; however, this approach was costly and errorprone @xcite . consequently, recent studies have been exploring the application of deep learning in automatic icd coding @xcite @xcite . however, there are noticeable gaps in the research on automatic icd coding across countries for various reasons. one reason is the difference in icd systems between nations. each country has a unique medical environment and requirements, with the result that many countries use modified icd systems @xcite @xcite . for instance, in korea, the korean standard classification of diseases (kcd) was developed. therefore, a specific model or algorithm may not always be effective and the development of models that consider each nation's icd system is essential. another reason for these disparities is differences in linguistic characteristics. each language possesses its own grammar, syntax, and semantic structure, which affect medical terms and their expression. hence, models that do not consider these characteristics may exhibit limited performance. lastly, resource availability also plays an important role. resource-rich countries use large-scale clinical data and systematic medical knowledge resources @xcite to accelerate automatic icd coding research @xcite . in contrast, in countries with limited data and resources, research progress may be slower or more constrained. to bridge the disparities in research levels across countries and enhance korea's constrained clinical natural language processing research environment, we propose a comprehensive approach: the development of a new dataset for automatic kcd coding and the implementation of a tokenization method tailored for korean clinical documents. first, we addressed the lack of korean datasets for automatic kcd coding by collecting and preprocessing clinical documents with assigned kcd codes from korean clinical environments. subsequently, we employed an optimized tokenization method to ensure that the automatic kcd coding model accurately captured the linguistic characteristics in the korean clinical documents. our contributions are as follows: the most renowned dataset for automatic icd coding is mimic-iii @xcite , an english dataset collected from the intensive care unit. furthermore, datasets are available for countries with relatively abundant language resources, such as china @xcite and spain @xcite . however, there is no such dataset in korea, which makes conducting research challenging. to address this issue, we constructed the korean dataset for automated kcd coding (kodak) for automatic kcd coding. in this section, we describe the process of constructing the kodak. ## conclusion and future work in this study, we introduced the kodak as a resource for facilitating automatic kcd coding research in korea, where the lack of suitable datasets has hindered such research. furthermore, we proposed a tokenization method that effectively reflects the linguistic features of korean clinical documents, thereby ensuring an accurate representation of crucial medical terms. our approach outperformed km-bert, achieving a 0.14%p improvement in macro-f1 while utilizing fewer parameters and without pre-training. in future research, we aim to address the unbalanced label distribution in the kodak and develop an enhanced pre-trained language model specifically designed for the korean clinical field.