@inproceedings{chen-etal-2026-krel,
title = "{KREL}: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with {LLM}s",
author = "Chen, Xubin and
Zhou, Yipeng and
Sun, Wen and
Huang, Chengkai and
Fu, Xiaoming and
Sheng, Quan Z.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1504/",
pages = "32024--32039",
ISBN = "979-8-89176-462-0",
abstract = "Automatic Medical Coding (AMC), which assigns standardized International Classification of Diseases (ICD) codes to clinical notes, is essential for medical reimbursement, quality reporting, and clinical research. Existing pre-trained language model (PLM)-based methods typically formulate AMC as an extreme multi-label classification problem over a predefined code set, while recent large language model (LLM)-based approaches instead frame it as generation or multi-step reasoning. However, key challenges remain, including the extreme length of clinical notes that hinders effective interpretation, the vast ICD label space, and complex coding rules that are not explicitly captured by LLMs. In this work, we propose Knowledge-Guided Reasoning over Clinical Evidence with LLMs (KREL), a framework that leverages LLMs for clinical text understanding and reasoning while integrating selected structured coding relations as external knowledge. This design enables tight coupling between domain knowledge and LLM reasoning, reducing hallucinations and supporting rule-aware verification. Experiments on benchmark datasets show that KREL consistently outperforms strong PLM-based and state-of-the-art LLM-based baselines."
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<abstract>Automatic Medical Coding (AMC), which assigns standardized International Classification of Diseases (ICD) codes to clinical notes, is essential for medical reimbursement, quality reporting, and clinical research. Existing pre-trained language model (PLM)-based methods typically formulate AMC as an extreme multi-label classification problem over a predefined code set, while recent large language model (LLM)-based approaches instead frame it as generation or multi-step reasoning. However, key challenges remain, including the extreme length of clinical notes that hinders effective interpretation, the vast ICD label space, and complex coding rules that are not explicitly captured by LLMs. In this work, we propose Knowledge-Guided Reasoning over Clinical Evidence with LLMs (KREL), a framework that leverages LLMs for clinical text understanding and reasoning while integrating selected structured coding relations as external knowledge. This design enables tight coupling between domain knowledge and LLM reasoning, reducing hallucinations and supporting rule-aware verification. Experiments on benchmark datasets show that KREL consistently outperforms strong PLM-based and state-of-the-art LLM-based baselines.</abstract>
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%0 Conference Proceedings
%T KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs
%A Chen, Xubin
%A Zhou, Yipeng
%A Sun, Wen
%A Huang, Chengkai
%A Fu, Xiaoming
%A Sheng, Quan Z.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-krel
%X Automatic Medical Coding (AMC), which assigns standardized International Classification of Diseases (ICD) codes to clinical notes, is essential for medical reimbursement, quality reporting, and clinical research. Existing pre-trained language model (PLM)-based methods typically formulate AMC as an extreme multi-label classification problem over a predefined code set, while recent large language model (LLM)-based approaches instead frame it as generation or multi-step reasoning. However, key challenges remain, including the extreme length of clinical notes that hinders effective interpretation, the vast ICD label space, and complex coding rules that are not explicitly captured by LLMs. In this work, we propose Knowledge-Guided Reasoning over Clinical Evidence with LLMs (KREL), a framework that leverages LLMs for clinical text understanding and reasoning while integrating selected structured coding relations as external knowledge. This design enables tight coupling between domain knowledge and LLM reasoning, reducing hallucinations and supporting rule-aware verification. Experiments on benchmark datasets show that KREL consistently outperforms strong PLM-based and state-of-the-art LLM-based baselines.
%U https://aclanthology.org/2026.findings-emnlp.1504/
%P 32024-32039
Markdown (Informal)
[KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs](https://aclanthology.org/2026.findings-emnlp.1504/) (Chen et al., Findings 2026)
ACL