Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification

Xindi Wang, Robert E. Mercer, Frank Rudzicz

Abstract
The International Classification of Diseases (ICD) is an authoritative medical classification system of different diseases and conditions for clinical and management purposes. ICD indexing aims to assign a subset of ICD codes to a medical record. Since human coding is labour-intensive and error-prone, many studies employ machine learning techniques to automate the coding process. ICD coding is a challenging task, as it needs to assign multiple codes to each medical document from an extremely large hierarchically organized collection. In this paper, we propose a novel approach for ICD indexing that adopts three ideas: (1) we use a multi-level deep dilated residual convolution encoder to aggregate the information from the clinical notes and learn document representations across different lengths of the texts; (2) we formalize the task of ICD classification with auxiliary knowledge of the medical records, which incorporates not only the clinical texts but also different clinical code terminologies and drug prescriptions for better inferring the ICD codes; and (3) we introduce a graph convolutional network to leverage the co-occurrence patterns among ICD codes, aiming to enhance the quality of label representations. Experimental results show the proposed method achieves state-of-the-art performance on a number of measures.
Anthology ID:
2024.lrec-main.181
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
2006–2016
Language:
URL:
https://aclanthology.org/2024.lrec-main.181
DOI:
Bibkey:
Cite (ACL):
Xindi Wang, Robert E. Mercer, and Frank Rudzicz. 2024. Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2006–2016, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification (Wang et al., LREC-COLING 2024)
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