@inproceedings{alliheedi-etal-2026-zero,
    title = "Zero-Shot, Fine-Tuned, and Retrieval-Augmented Extraction of Clinical Decisions with Corpus Boundary Diagnostics",
    author = "Alliheedi, Mohammed  and
      Mercer, Robert  and
      Machina, Anemily  and
      Roy, Sudipta  and
      Wang, Yetian  and
      Wang, Xindi",
    editor = "Gupta, Deepak  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the {B}io{NLP} 2026 (Shared Tasks)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-2.20/",
    pages = "141--145",
    ISBN = "979-8-89176-435-4",
    abstract = "We present the CanSA system for the MedEx-ACT@ACL 2026 shared task, which requires extracting and classifying clinical decisions from ICU discharge summaries into nine DIC-TUM categories. We have developed three approaches: (1) a training-free system which consists of a preprocessing module that normalizes text and an inference engine combining zero shot LLMs with a RAG ensemble, (2) a supervised fine-tuning method which required training, and (3) a training-free retrieval-augmented pipeline employing TF{--}IDF-based lexical retrieval to surface in-context exemplars from the development corpus, combined with section aware chunking and structured extraction calls to a large language model. Our team{'}s best submission achieved a Final Score of 0.41, ranking 34th out of 37 on the official test leaderboard."
}