@inproceedings{zhang-etal-2022-section,
    title = "Section Classification in Clinical Notes with Multi-task Transformers",
    author = "Zhang, Fan  and
      Laish, Itay  and
      Benjamini, Ayelet  and
      Feder, Amir",
    editor = "Lavelli, Alberto  and
      Holderness, Eben  and
      Jimeno Yepes, Antonio  and
      Minard, Anne-Lyse  and
      Pustejovsky, James  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.louhi-1.7/",
    doi = "10.18653/v1/2022.louhi-1.7",
    pages = "54--59",
    abstract = "Clinical notes are the backbone of electronic health records, often containing vital information not observed in other structured data. Unfortunately, the unstructured nature of clinical notes can lead to critical patient-related information being lost. Algorithms that organize clinical notes into distinct sections are often proposed in order to allow medical professionals to better access information in a given note. These algorithms, however, often assume a given partition over the note, and classify section types given this information. In this paper, we propose a multi-task solution for note sectioning, where a single model identifies context changes and labels each section with its medically-relevant title. Results on in-distribution (MIMIC-III) and out-of-distribution (private held-out) datasets reveal that our approach successfully identifies note sections across different hospital systems."
}