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.- Anthology ID:
- 2022.bionlp-1.33
- Volume:
- Proceedings of the 21st Workshop on Biomedical Language Processing
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 337–344
- Language:
- URL:
- https://aclanthology.org/2022.bionlp-1.33
- DOI:
- 10.18653/v1/2022.bionlp-1.33
- Cite (ACL):
- Hillary Ngai and Frank Rudzicz. 2022. Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 337–344, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation (Ngai & Rudzicz, BioNLP 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.bionlp-1.33.pdf
- Code
- hillary-ngai/doctor_xavier