@inproceedings{feucht-etal-2021-description,
    title = "Description-based Label Attention Classifier for Explainable {ICD}-9 Classification",
    author = "Feucht, Malte  and
      Wu, Zhiliang  and
      Althammer, Sophia  and
      Tresp, Volker",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
    month = nov,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.wnut-1.8/",
    doi = "10.18653/v1/2021.wnut-1.8",
    pages = "62--66",
    abstract = "ICD-9 coding is a relevant clinical billing task, where unstructured texts with information about a patient`s diagnosis and treatments are annotated with multiple ICD-9 codes. Automated ICD-9 coding is an active research field, where CNN- and RNN-based model architectures represent the state-of-the-art approaches. In this work, we propose a description-based label attention classifier to improve the model explainability when dealing with noisy texts like clinical notes."
}