Automated Generation of ICD-11 Cluster Codes for Precision Medical Record Classification

Jiayi Feng; Runtong Zhang; Donghua Chen; Lei Shi; Zhaoxing Li

doi:10.15837/ijccc.2024.1.6251

Authors

Jiayi Feng Department of Information Management, Beijing Jiaotong University, China
Runtong Zhang Department of Information Management, Beijing Jiaotong University, China
Donghua Chen Department of Information Management, Beijing Jiaotong University, China
Lei Shi School of Computing, Newcastle University, United Kingdom
Zhaoxing Li Department of Electronics and Computer Science, University of Southampton, United Kingdom

DOI:

https://doi.org/10.15837/ijccc.2024.1.6251

Keywords:

ICD-11;ICD code; machine learning; text similarity; clinical coding;

Abstract

Accurate clinical coding using the International Classification of Diseases (ICD) standard is essential for healthcare analytics. ICD-11 introduces new coding guidelines and cluster structures, posing challenges for existing coding tools. This research presents an automated approach to generate valid ICD-11 cluster codes from medical text. Natural language records are represented as vectors and compared to an ICD-11 corpus using cosine similarity. A bidirectional matching technique then refines similarity estimation. Experiments demonstrate the method yields up to 0.91 F1 score in coding accuracy, significantly outperforming a baseline tool. This work enables efficient high-quality ICD-11 coding to support healthcare informatics.

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