信息增强的医患对话理解

张智林,陈文亮

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PDF(1950 KB)
中文信息学报 ›› 2023, Vol. 37 ›› Issue (1) : 121-131.
问答与对话

信息增强的医患对话理解

  • 张智林,陈文亮
作者信息 +

Information-enhanced Understanding of the Doctor-patient Dialogue

  • ZHANG Zhilin,CHEN Wenliang
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摘要

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。

Abstract

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.

关键词

医患对话理解 / 特征增强 / 语义融合

Key words

doctor-patient conversation understanding / feature enhancement / semantic fusion

引用本文

导出引用
张智林,陈文亮. 信息增强的医患对话理解. 中文信息学报. 2023, 37(1): 121-131
ZHANG Zhilin,CHEN Wenliang. Information-enhanced Understanding of the Doctor-patient Dialogue. Journal of Chinese Information Processing. 2023, 37(1): 121-131

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基金

国家自然科学基金(61936010)
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