Abstract
Automated Medical Coding (AMC) is the task of automatically converting free-text medical documents into predefined codes according to a specific medical coding system. Although deep learning has significantly advanced AMC, the class imbalance problem remains a significant challenge. To address this issue, most existing methods consider only a single coding system and disregard the potential benefits of reflecting the relevance between different coding systems. To bridge this gap, we introduce a Joint learning framework for Across Medical coding Systems (JAMS), which jointly learns different coding systems through multi-task learning. It learns various representations using a shared encoder and explicitly captures the relationships across these coding systems using the medical code attention network, a modification of the graph attention network. In the experiments on the MIMIC-IV ICD-9 and MIMIC-IV ICD-10 datasets, connected through General Equivalence Mappings, JAMS improved the performance consistently regardless of the backbone models. This result demonstrates its model-agnostic characteristic, which is not constrained by specific model structures. Notably, JAMS significantly improved the performance of low-frequency codes. Our analysis shows that these performance gains are due to the connections between the codes of the different coding systems.- Anthology ID:
- 2024.lrec-main.227
- 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:
- 2520–2525
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.227
- DOI:
- Cite (ACL):
- Geunyeong Jeong, Seokwon Jeong, Juoh Sun, and Harksoo Kim. 2024. Bridging the Code Gap: A Joint Learning Framework across Medical Coding Systems. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2520–2525, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Bridging the Code Gap: A Joint Learning Framework across Medical Coding Systems (Jeong et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/landing_page/2024.lrec-main.227.pdf