信息增强的医患对话理解

Information-enhanced Understanding of the Doctor-patient Dialogue

近年来在线问诊的需求日益增大,亟需关于自动化医疗问诊方面的研究,而医患对话理解是智能医疗研究的基础。然而在真实场景中,医患对话理解面临着实体表述复杂、状态判断困难的问题。针对这些问题,该文提出一种信息增强的医患对话理解模型,该模型强调医患对话中的角色特征和症状特征用于增强文本信息,并将症状实体语义和阅读理解语义融合用于丰富语义信息。基于所提出模型的系统在第一届智能对话诊疗评测——医患对话理解测试集上取得了91.7%的命名实体识别F1值和73.7%的症状状态识别F1值。

The doctor-patient dialogue understanding is a typical task in intelligent medical community,which is challenged by entity representation and state determination.This paper proposes an information-enhanced doctor-patient dialogue understanding model.The model emphasizes the role features and symptom features,and integrates the semantics of symptom entities and reading comprehension semantics to enrich doctor-patient dialogue representation.On the first Intelligent Dialogue Diagnostic Assessment-Doctor-Patient Dialogue Understanding test set,the proposed model achieved 91.7% F1 for named entity recognition and 73.7% F1 for symptom state recognition.