@inproceedings{sinha-etal-2026-medical,
    title = "Medical Context Variation: A source of impairment for Event classification",
    author = "Sinha, Aman  and
      Clausel, Marianne  and
      Constant, Mathieu  and
      Coubez, Xavier",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.70/",
    pages = "864--879",
    ISBN = "979-8-89176-434-7",
    abstract = "The variation in writing style encapsulates nuanced characteristics, which are often exploited for author or demographic identification. In the medical domain, language models are frequently deployed to capture relevant information from unstructured or complex data, such as clinical notes that often include patients' medical histories. Such data is largely free-form and unstructured, obtained through diverse clinician?patient interactions. In this work, we present a case study investigating whether variations in clinicians' writing styles can lead to differences in medical context understanding capabilities for pre-trained language models (PLMs) on downstream tasks, such as medical event classification. Our findings indicate that variation in writing style, characterized by linguistic features, can indeed lead to suboptimal performance in deployed systems. Furthermore, we explore linguistic guided counterfactual reasoning in order to mitigate the impact of writing style variation which suggests LLM-based stylistic normalization to be effective for this purpose."
}