@inproceedings{793ba3d83c934652a9174be42245406e,
title = "Optimizing Automated KCD Coding: A Retrieval-Verification Approach",
abstract = "This study proposes a two-step Retrieval-Verification system for automating the assignment of Korean Standard Classification of Diseases (KCD) codes to free-text diagnoses. The system uses SapBERT-XLMR for initial retrieval, followed by Llama 3.1 for final verification and code selection. Combining the two models improved accuracy to 82.3\%. Future work aims to improve the system{\textquoteright}s performance on abbreviations and conduct experiment with a larger dataset.",
keywords = "Clinical coding, Embedding, KCD, Language models",
author = "Sangji Lee and Cha, \{Won Chul\}",
note = "Publisher Copyright: {\textcopyright} 2025 The Authors.; 35th Medical Informatics Europe Conference, MIE 2025 ; Conference date: 19-05-2025 Through 21-05-2025",
year = "2025",
month = may,
day = "15",
doi = "10.3233/SHTI250485",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "872--873",
editor = "Elisavet Andrikopoulou and Parisis Gallos and Arvanitis, \{Theodoros N.\} and Rosalynn Austin and Arriel Benis and Ronald Cornet and Panagiotis Chatzistergos and Alexander Dejaco and Linda Dusseljee-Peute and Alaa Mohasseb and Pantelis Natsiavas and Haythem Nakkas and Philip Scott",
booktitle = "Intelligent Health Systems - From Technology to Data and Knowledge, Proceedings of MIE 2025",
}