@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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation
%A Ngai, Hillary
%A Rudzicz, Frank
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 21st Workshop on Biomedical Language Processing
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ngai-rudzicz-2022-doctor
%X 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.
%R 10.18653/v1/2022.bionlp-1.33
%U https://aclanthology.org/2022.bionlp-1.33
%U https://doi.org/10.18653/v1/2022.bionlp-1.33
%P 337-344
Markdown (Informal)
[Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation](https://aclanthology.org/2022.bionlp-1.33) (Ngai & Rudzicz, BioNLP 2022)
ACL