Concept-Enhanced Automatic ICD Coding using Large Language Models

Md Shahrar Fatemi, Zhan Shi, Joel Saltz, Klaus Mueller, Tengfei Ma
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:921-935, 2026.

Abstract

Automatic {ICD} coding is a task which assigns disease or procedure codes to clinical notes from patients’ electronic health record data. Large language models have been explored for this task, but none of the existing approaches have shown stronger performance than traditional deep learning models due to limited ability to model concepts. Existing methods for {ICD} coding often utilize the code descriptions or synonyms to enhance performance. In this paper, we propose to use concepts to expand the label space. Utilizing the hierarchy of {ICD} codes, we construct concepts associated with the codes at different levels, and employ fine-tuned large language models to obtain concept scores, which are then used for code prediction. Experiments conducted on {MIMIC}-{III}-50, and {MIMIC}-{III}-rare50 datasets demonstrate that our models achieve excellent performance and largely outperform previous state-of-the-art models. While the current evaluation is constrained in scope and computational tractability, the results provide strong evidence for the potential of concept-driven {LLM} frameworks to advance automated medical coding.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-fatemi26a, title = {Concept-Enhanced Automatic {ICD} Coding using Large Language Models}, author = {Fatemi, Md Shahrar and Shi, Zhan and Saltz, Joel and Mueller, Klaus and Ma, Tengfei}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {921--935}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/fatemi26a/fatemi26a.pdf}, url = {https://proceedings.mlr.press/v297/fatemi26a.html}, abstract = {Automatic {ICD} coding is a task which assigns disease or procedure codes to clinical notes from patients’ electronic health record data. Large language models have been explored for this task, but none of the existing approaches have shown stronger performance than traditional deep learning models due to limited ability to model concepts. Existing methods for {ICD} coding often utilize the code descriptions or synonyms to enhance performance. In this paper, we propose to use concepts to expand the label space. Utilizing the hierarchy of {ICD} codes, we construct concepts associated with the codes at different levels, and employ fine-tuned large language models to obtain concept scores, which are then used for code prediction. Experiments conducted on {MIMIC}-{III}-50, and {MIMIC}-{III}-rare50 datasets demonstrate that our models achieve excellent performance and largely outperform previous state-of-the-art models. While the current evaluation is constrained in scope and computational tractability, the results provide strong evidence for the potential of concept-driven {LLM} frameworks to advance automated medical coding.} }
Endnote
%0 Conference Paper %T Concept-Enhanced Automatic ICD Coding using Large Language Models %A Md Shahrar Fatemi %A Zhan Shi %A Joel Saltz %A Klaus Mueller %A Tengfei Ma %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-fatemi26a %I PMLR %P 921--935 %U https://proceedings.mlr.press/v297/fatemi26a.html %V 297 %X Automatic {ICD} coding is a task which assigns disease or procedure codes to clinical notes from patients’ electronic health record data. Large language models have been explored for this task, but none of the existing approaches have shown stronger performance than traditional deep learning models due to limited ability to model concepts. Existing methods for {ICD} coding often utilize the code descriptions or synonyms to enhance performance. In this paper, we propose to use concepts to expand the label space. Utilizing the hierarchy of {ICD} codes, we construct concepts associated with the codes at different levels, and employ fine-tuned large language models to obtain concept scores, which are then used for code prediction. Experiments conducted on {MIMIC}-{III}-50, and {MIMIC}-{III}-rare50 datasets demonstrate that our models achieve excellent performance and largely outperform previous state-of-the-art models. While the current evaluation is constrained in scope and computational tractability, the results provide strong evidence for the potential of concept-driven {LLM} frameworks to advance automated medical coding.
APA
Fatemi, M.S., Shi, Z., Saltz, J., Mueller, K. & Ma, T.. (2026). Concept-Enhanced Automatic ICD Coding using Large Language Models. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:921-935 Available from https://proceedings.mlr.press/v297/fatemi26a.html.

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