@inproceedings{chen-etal-2025-emrs2csp,
    title = "{EMR}s2{CSP} : Mining Clinical Status Pathway from Electronic Medical Records",
    author = "Chen, Yifei  and
      Hou, Ruihui  and
      Liu, Jingping  and
      Ruan, Tong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.findings-acl.886/",
    doi = "10.18653/v1/2025.findings-acl.886",
    pages = "17235--17251",
    ISBN = "979-8-89176-256-5",
    abstract = "Many current studies focus on extracting tests or treatments when constructing clinical pathways, often neglecting the patient{'}s symptoms and diagnosis, leading to incomplete diagnostic and therapeutic logic. Therefore, this paper aims to extract clinical pathways from electronic medical records that encompass complete diagnostic and therapeutic logic, including temporal information, patient symptoms, diagnosis, and tests or treatments. To achieve this objective, we propose a novel clinical pathway representation: the clinical status pathway. We also design a LLM-based pipeline framework for extracting clinical status pathway from electronic medical records, with the core concept being to improve extraction accuracy by modeling the diagnostic and treatment processes. In our experiments, we apply this framework to construct a comprehensive breast cancer-specific clinical status pathway and evaluate its performance on medical question-answering and decision-support tasks, demonstrating significant improvements over traditional clinical pathways. The code is publicly available at https://github.com/finnchen11/EMRs2CSP."
}