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.

介绍了一种基于 BERT 的诊断系统 Doctor XAvIer，能够从病人和医生的对话中提取诊断所需数据并利用特征归因方法来解释预测结果。通过特征归因松弛曲线和其归一化曲线下面积来评价特征归因方法的性能，分析表明综合梯度比 Shapley 值更胜一筹，并在命名实体识别和症状关联分类上取得 0.97 F1 分数，在诊断分类上取得 0.91 F1 分数，优于基线系统。