@inproceedings{parviz-etal-2025-bias,
    title = "Bias in {D}anish Medical Notes: Infection Classification of Long Texts Using Transformer and {LSTM} Architectures Coupled with {BERT}",
    author = "Parviz, Mehdi  and
      Agius, Rudi  and
      Niemann, Carsten  and
      Van Der Goot, Rob",
    editor = "Ananiadou, Sophia  and
      Demner-Fushman, Dina  and
      Gupta, Deepak  and
      Thompson, Paul",
    booktitle = "Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.cl4health-1.27/",
    pages = "316--320",
    ISBN = "979-8-89176-238-1",
    abstract = "Medical notes contain a wealth of information related to diagnosis, prognosis, and overall patient care that can be used to help physicians make informed decisions. However, like any other data sets consisting of data from diverse demographics, they may be biased toward certain subgroups or subpopulations. Consequently, any bias in the data will be reflected in the output of the machine learning models trained on them. In this paper, we investigate the existence of such biases in Danish medical notes related to three types of blood cancer, with the goal of classifying whether the medical notes indicate severe infection. By employing a hierarchical architecture that combines a sequence model (Transformer and LSTM) with a BERT model to classify long notes, we uncover biases related to demographics and cancer types. Furthermore, we observe performance differences between hospitals. These findings underscore the importance of investigating bias in critical settings such as healthcare and the urgency of monitoring and mitigating it when developing AI-based systems."
}