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

Lookup NU author(s): Dr Lei ShiORCiD

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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.

Publication metadata

Author(s): Feng J, Zhang R, Chen D, Shi L, Li Z

Publication type: Article

Publication status: Published

Journal: International Journal of Computers Communications & Control

Year: 2024

Volume: 19

Issue: 1

Print publication date: 01/02/2024

Online publication date: 04/01/2024

Acceptance date: 23/11/2023

Date deposited: 07/01/2024

ISSN (print): 1841-9836

ISSN (electronic): 1841-9844

Publisher: Universitatea Agora

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

DOI: 10.15837/ijccc.2024.1.6251

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