@inproceedings{bohr-etal-2025-perspective,
    title = "Perspective: Leveraging Domain Knowledge for Tabular Machine Learning in the Medical Domain",
    author = "Bohr, Arijana  and
      Altstidl, Thomas  and
      Eskofier, Bjoern  and
      Salin, Emmanuelle",
    editor = "Chang, Shuaichen  and
      Hulsebos, Madelon  and
      Liu, Qian  and
      Chen, Wenhu  and
      Sun, Huan",
    booktitle = "Proceedings of the 4th Table Representation Learning Workshop",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.trl-workshop.11/",
    pages = "143--155",
    ISBN = "979-8-89176-268-8",
    abstract = "There has been limited exploration of how to effectively integrate domain knowledge into machine learning for medical tabular data.Traditional approaches often rely on non-generalizable processes tailored to specific datasets.In contrast, recent advances in deep learning for language and tabular data are leading the way toward more generalizable and scalable methods of domain knowledge inclusion. In this paper, we first explore the need for domain knowledge in medical tabular data, categorize types of medical domain knowledge, and discuss how each can be leveraged in tabular machine learning. We then outline strategies for integrating this knowledge at various stages of the machine learning pipeline. Finally, building on recent advances in tabular deep learning, we propose future research directions to support the integration of domain knowledge."
}