@inproceedings{elgaar-etal-2025-meddecxtract,
    title = "{M}ed{D}ec{X}tract: A Clinician-Support System for Extracting, Visualizing, and Annotating Medical Decisions in Clinical Narratives",
    author = "Elgaar, Mohamed  and
      Amiri, Hadi  and
      Mohtarami, Mitra  and
      Celi, Leo Anthony",
    editor = "Mishra, Pushkar  and
      Muresan, Smaranda  and
      Yu, Tao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.46/",
    pages = "481--489",
    ISBN = "979-8-89176-253-4",
    abstract = "Clinical notes contain crucial information about medical decisions, including diagnosis, treatment choices, and follow-up plans. However, these decisions are embedded within unstructured text, making it challenging to systematically analyze decision-making patterns or support clinical workflows. We present MedDecXtract, an open-source interactive system that automatically extracts and visualizes medical decisions from clinical text. The system combines a RoBERTa-based model for identifying ten categories of medical decisions (e.g., diagnosis, treatment, follow-up) according to the DICTUM framework, with an intuitive interface for exploration, visualization, and annotation. The system enables various applications including clinical decision support, research on decision patterns, and creation of training data for improved medical language models. The system and its source code can be accessed at https://mohdelgaar-clinical-decisions.hf.space. A video demo is available at https://youtu.be/19j6-XtIE{\_}s."
}