@inproceedings{ngai-rudzicz-2022-doctor,
    title = "Doctor {XA}v{I}er: Explainable Diagnosis on Physician-Patient Dialogues and {XAI} Evaluation",
    author = "Ngai, Hillary  and
      Rudzicz, Frank",
    editor = "Demner-Fushman, Dina  and
      Cohen, Kevin Bretonnel  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 21st Workshop on Biomedical Language Processing",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2022.bionlp-1.33/",
    doi = "10.18653/v1/2022.bionlp-1.33",
    pages = "337--344",
    abstract = "We introduce Doctor XAvIer {---} a BERT-based diagnostic system that extracts relevant clinical data from transcribed patient-doctor dialogues and explains predictions using feature attribution methods. We present a novel performance plot and evaluation metric for feature attribution methods {---} Feature Attribution Dropping (FAD) curve and its Normalized Area Under the Curve (N-AUC). FAD curve analysis shows that integrated gradients outperforms Shapley values in explaining diagnosis classification. Doctor XAvIer outperforms the baseline with 0.97 F1-score in named entity recognition and symptom pertinence classification and 0.91 F1-score in diagnosis classification."
}