Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation

Autor:	Ngai, Hillary, Rudzicz, Frank
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.10178 Zobrazit plný text záznamu View this record from Arxiv