@inproceedings{huang-etal-2026-towards,
    title = "Towards Explainable Diagnosis: A Self-learned Explanatory Knowledge Base Approach",
    author = "Huang, Dongqi  and
      Zhou, Tong  and
      Jin, Zhuoran  and
      Shi, Shenghui  and
      Maoyujiao  and
      Liu, Kang  and
      Zhao, Jun  and
      Chen, Yubo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1700/",
    pages = "36679--36700",
    ISBN = "979-8-89176-390-6",
    abstract = "Explainable diagnosis requires that authoritative medical knowledge provide the rationales linking a patient{'}s clinical manifestations to the diagnostic conclusion. Although large language models (LLMs) hold great potential to facilitate explainable diagnosis, their effectiveness is often constrained by insufficient diagnostic expertise. To address this limitation, we propose Self-learned Explainable Knowledge Augmented Diagnosis (SEKAD), a unified LLM-based framework for faithful and explainable diagnosis. Our approach builds a high-quality diagnostic knowledge base through a record-driven explanation learning paradigm, as well as applies this knowledge via an explanation-based diagnostic process that ensures faithful inference. Experiments on the DiReCT and JAMA benchmarks show that SEKAD consistently outperforms strong baselines across the metrics. In particular, on the DiReCT benchmark, SEKAD improves the explanation completeness metric from 64.5{\%} to 76.9{\%} over the best existing methods, highlighting its effectiveness in enhancing diagnostic explainability and showing that our text mining approach produces knowledge that is both reliable in quality and large in quantity."
}