论文标题
Xavier医生：可解释的医师对话和XAI评估的诊断
Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation
论文作者
论文摘要
我们介绍了基于BERT的诊断系统Xavier医生，该系统从转录的患者doctor对话中提取相关的临床数据，并使用功能归因方法解释了预测。我们提出了特征归因方法的新型性能图和评估度量：特征归因降低（FAD）曲线及其在曲线下的归一化区域（N-AUC）。 FAD曲线分析表明，综合梯度在解释诊断分类方面优于沙普利值。 Xavier医生在指定的实体识别和症状性相关分类中以0.97 F1分数和0.91 F1分类中的基线优于基线。
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.
