@inproceedings{jeong-etal-2023-improving,
    title = "Improving Automatic {KCD} Coding: Introducing the {K}o{DAK} and an Optimized Tokenization Method for {K}orean Clinical Documents",
    author = "Jeong, Geunyeong  and
      Sun, Juoh  and
      Jeong, Seokwon  and
      Shin, Hyunjin  and
      Kim, Harksoo",
    editor = "Naumann, Tristan  and
      Ben Abacha, Asma  and
      Bethard, Steven  and
      Roberts, Kirk  and
      Rumshisky, Anna",
    booktitle = "Proceedings of the 5th Clinical Natural Language Processing Workshop",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.clinicalnlp-1.12",
    doi = "10.18653/v1/2023.clinicalnlp-1.12",
    pages = "96--101",
    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.",
}

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.
Jeong, Geunyeong Sun, Juoh Jeong, Seokwon Shin, Hyunjin Kim, Harksoo
Improving Automatic KCD Coding: Introducing the KoDAK and an Optimized Tokenization Method for Korean Clinical Documents