@inproceedings{zeng-etal-2026-lung,
title = "{L}ung-{R}1: A Knowledge Graph-Guided {LLM} for Pulmonary Diagnostic Reasoning",
author = "Zeng, Haoyang and
Fu, Yuanxi and
Li, Rongzhen and
Ymyang and
Sun, Xiao and
Huang, Jingwang and
Shao, Gujie and
Xiang, Guohui and
Lu, Quan and
Dongfan, Ye and
Chen, Xuetao and
Zhong, Jiang and
Zhi, Xu and
Wei, Kaiwen",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1100/",
pages = "25223--25244",
ISBN = "979-8-89176-459-0",
abstract = "Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap. Although large language models (LLMs) have shown progress on pulmonary knowledge question answering (QA) and information-processing tasks, reliable pulmonary diagnosis requires patient-specific, relation-aware reasoning over electronic medical record (EMR) evidence rather than isolated knowledge recall. We define this gap between pulmonary knowledge and case-level diagnostic reasoning as the Pulmonary Knowledge-to-Diagnosis Gap. To address it, we introduce LungKG, the first structured pulmonary knowledge graph for diagnostic knowledge organization and record-grounded reasoning. LungKG contains 59,038 nodes and 164,308 edges across 15 entity types and 112 relation types, serving as both a reusable pulmonary knowledge resource and the foundation for LungKG-guided model adaptation. Built on LungKG, we propose Lung-R1, a LungKG-guided pulmonary LLM trained through KG-constrained reasoning-chain construction and KG-guided reinforcement learning. In a 20-system evaluation, Lung-R1-14B achieves state-of-the-art performance across Choice, Pulmonary-QA, and EMR Diagnosis, reaching an EMR Diagnosis score of 4.3583 and surpassing the strongest non-Lung-R1 baseline by 0.1476 points. These results demonstrate the value of LungKG-guided training for EMR-based pulmonary diagnosis. The dataset and code are available at GitHub repository."
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<abstract>Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap. Although large language models (LLMs) have shown progress on pulmonary knowledge question answering (QA) and information-processing tasks, reliable pulmonary diagnosis requires patient-specific, relation-aware reasoning over electronic medical record (EMR) evidence rather than isolated knowledge recall. We define this gap between pulmonary knowledge and case-level diagnostic reasoning as the Pulmonary Knowledge-to-Diagnosis Gap. To address it, we introduce LungKG, the first structured pulmonary knowledge graph for diagnostic knowledge organization and record-grounded reasoning. LungKG contains 59,038 nodes and 164,308 edges across 15 entity types and 112 relation types, serving as both a reusable pulmonary knowledge resource and the foundation for LungKG-guided model adaptation. Built on LungKG, we propose Lung-R1, a LungKG-guided pulmonary LLM trained through KG-constrained reasoning-chain construction and KG-guided reinforcement learning. In a 20-system evaluation, Lung-R1-14B achieves state-of-the-art performance across Choice, Pulmonary-QA, and EMR Diagnosis, reaching an EMR Diagnosis score of 4.3583 and surpassing the strongest non-Lung-R1 baseline by 0.1476 points. These results demonstrate the value of LungKG-guided training for EMR-based pulmonary diagnosis. The dataset and code are available at GitHub repository.</abstract>
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%0 Conference Proceedings
%T Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning
%A Zeng, Haoyang
%A Fu, Yuanxi
%A Li, Rongzhen
%A Sun, Xiao
%A Huang, Jingwang
%A Shao, Gujie
%A Xiang, Guohui
%A Lu, Quan
%A Dongfan, Ye
%A Chen, Xuetao
%A Zhong, Jiang
%A Zhi, Xu
%A Wei, Kaiwen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Ymyang
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zeng-etal-2026-lung
%X Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap. Although large language models (LLMs) have shown progress on pulmonary knowledge question answering (QA) and information-processing tasks, reliable pulmonary diagnosis requires patient-specific, relation-aware reasoning over electronic medical record (EMR) evidence rather than isolated knowledge recall. We define this gap between pulmonary knowledge and case-level diagnostic reasoning as the Pulmonary Knowledge-to-Diagnosis Gap. To address it, we introduce LungKG, the first structured pulmonary knowledge graph for diagnostic knowledge organization and record-grounded reasoning. LungKG contains 59,038 nodes and 164,308 edges across 15 entity types and 112 relation types, serving as both a reusable pulmonary knowledge resource and the foundation for LungKG-guided model adaptation. Built on LungKG, we propose Lung-R1, a LungKG-guided pulmonary LLM trained through KG-constrained reasoning-chain construction and KG-guided reinforcement learning. In a 20-system evaluation, Lung-R1-14B achieves state-of-the-art performance across Choice, Pulmonary-QA, and EMR Diagnosis, reaching an EMR Diagnosis score of 4.3583 and surpassing the strongest non-Lung-R1 baseline by 0.1476 points. These results demonstrate the value of LungKG-guided training for EMR-based pulmonary diagnosis. The dataset and code are available at GitHub repository.
%U https://aclanthology.org/2026.emnlp-main.1100/
%P 25223-25244
Markdown (Informal)
[Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning](https://aclanthology.org/2026.emnlp-main.1100/) (Zeng et al., EMNLP 2026)
ACL
- Haoyang Zeng, Yuanxi Fu, Rongzhen Li, Ymyang, Xiao Sun, Jingwang Huang, Gujie Shao, Guohui Xiang, Quan Lu, Ye Dongfan, Xuetao Chen, Jiang Zhong, Xu Zhi, and Kaiwen Wei. 2026. Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 25223–25244, Budapest, Hungary. Association for Computational Linguistics.