@inproceedings{baumgartner-schilling-2026-cuamc,
    title = "{CUAMC} @ {M}ed{E}x{ACT} 2026: Robust Ensemble Voting for Fair Medical Decision Extraction",
    author = "Baumgartner, William  and
      Schilling, Lisa",
    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-2.24/",
    pages = "171--178",
    ISBN = "979-8-89176-435-4",
    abstract = "Automated extraction of medical decisions from clinical notes is a critical step to constructing more granular patient health trajectories than what is currently obtainable from structured healthcare data. Here we present a system designed for the MedExACT shared task that employs an ensemble of BERT-based classifiers to account for demographic diversity when extracting mentions of medical decisions from MIMIC-III discharge summaries. A simple voting strategy combined with architectural diversity is demonstrated to work best when training data is limited."
}